The assumption of scene rigidity is typical in SLAM algorithms . Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments , which are the target of several relevant applications like service robotics or autonomous vehicles . In this letter we present DynaSLAM , a visual SLAM system that , building on ORB-SLAM2 , adds the capabilities of dynamic object detection and background inpainting . We implemented autonomous driving vehicle system at an intersection equipped with traffic lights . This system was consisted of a traffic light recognition , crossing/stop decision algorithm , vehicle localization , vehicle longitudinal/lateral control , and coordinate map generation . Now a days Autonomous electric vehicle are the current research trends in the automobile industry and future vehicle used by humans . However , the commercial realization of autonomous vehicles remains a significant challenge . The illumination invariant colour space stems from modelling the spectral properties of the camera and scene illumination in conjunction , and requires only a single parameter derived from the image sensor specifications . We present results using a 24-hour dataset collected using an autonomous road vehicle , demonstrating increased consistency of the illumination invariant images in comparison to raw RGB images during daylight hours . We then present three example applications of how illumination invariant imaging can improve performance in the context of vision-based autonomous vehicles : 6-DoF metric localisation using monocular cameras over a 24-hour period , life-long visual localisation and mapping using stereo , and urban scene classification in changing environments . This work serves as the proof of concept of an autonomous vehicle prototype developed by Moovita and Universiti Teknologi Malaysia . For a dependable driverless vehicle maneuver , it requires a stable velocity controller to allow for the desired longitudinal motion navigation . Advanced Driver Assistance Systems ( ADAS ) are essential parts for developing the autonomous vehicle concept . They cooperate with different on-board car equipment to make driving safe and comfortable . The system can make use of existing infrastructure , reduce urban sprawl and ease congestion . Autonomous vehicle based systems can improve fuel efficiency . The technology presents a window of opportunity for a new mode of transportation that obtains efficiencies of up to 0.25 l/100 km ( 1000 mpg equivalent ) , reducing U.S. petroleum consumption by up to 16 % . In this paper , we outline a methodology for evaluating the situation awareness ( SA ) provided by a supervisory interface for an autonomous on-road vehicle . Our goal is to be able to use the evaluations to compare interface designs with respect to how well each facilitates the users ' acquisition of SA . We present the new Road Event and Activity Detection ( READ ) dataset , designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving . READ will give scholars in computer vision , smart cars and machine learning at large the opportunity to conduct research into exciting new problems such as understanding complex ( road ) activities , discerning the behaviour of sentient agents , and predicting both the label and the location of future actions and events , with the final goal of supporting autonomous decision making . Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible . Many real - world robotic systems are equipped with multiple 3D sensors : for example , autonomous cars often have multiple lidars and potentially also radars . Yet , current algorithms often assume a single RGB - D camera , which provides RGB - D images , or a single lidar sensor , which allows the creation of a local front view image of the lidar depth and intensity readings . section : Introduction Since the DARPA Grand Challenges for autonomous vehicles , there has been an explosion in applications and research for self - driving cars . Among the different environments for self - driving cars , highway and urban roads are on opposite ends of the spectrum . The principal concerns in road safety are understanding and preventing risky situations . A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents . Improving vehicle stabilization is possible when vehicle parameters are known . The invention discloses an automobile safety protection device which is used for lowering damage degree of a car body in case of car crash . The automobile safety protection device is equipped with hydraulic columns and telescopic heads , wherein , top enclosures are sheathed on the front ends of the telescopic heads ; front springs are connected between the top enclosures and the telescopic heads ; locating slots are arranged inside the top enclosures ; the telescopic heads are equipped with bulges corresponding to the locating slots ; the front springs in the top enclosures are connected with bumpers ; air cushions are wrapped outside the bumpers ; gear sleeves are arranged outside the hydraulic columns and are connected with motor gears ; rear air bags are arranged at the tail parts of the hydraulic columns and are connected with tail springs ; and locating columns are also connected at the tail parts of the hydraulic columns . The results show that the head - neck finite element model without muscle tissue has a consistent mechanics and dynamics with the body drop test . The model can be applied to the exploration of a more detailed neck injury model with muscle tissue and can also be directly applied to the study of injury mechanism of the cervical spine in the car crash accidents . The car black box is a vehicle-based recorder which records various parameters like rotation of wheel , temperature of vehicle inside , battery voltage , seat belt and door lock observation . These data can be used for accurate car accident investigation and some public crimes prevention . Car black box is a digital electronic device which can record and store vehicle speed , in real-time and vehicle 's other status information . In recent years , the importance of driver ’s support system is increasing as a solution for dealing with car related accidents . These driving support systems are equipped with functions for avoiding various hazards when the driver drives the vehicle , reducing the risk of causing an accident . According to the statistics of car accident causes [ 11 ] given by Police of the Czech Republic , about 17.5 % of all car accidents are caused by lack of dedication to driving , including microsleep . A lot of car factories develop systems for fatigue prediction and microsleep detection . Road safety telematic systems are complex heterogeneous systems . The technology to build them exists and can save lives and reduce the severity of car accident injuries . However for the solution to be feasible , among others , it is necessary to achieve multi-vendor interoperability . The present invention relates to a central curtain airbag apparatus for a vehicle , which is used to protect the rear seat passenger airbag cushion during a car collision accident . To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios . We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system . This paper aims to highlight vision related tasks centered around “ car ” , which has been largely neglected by vision community in comparison to other objects . We show that there are still many interesting car - related problems and applications , which are not yet well explored and researched . To facilitate future car - related research , in this paper we present our on - going effort in collecting a large - scale dataset , “ CompCars ” , that covers not only different car views , but also their different internal and external parts , and rich attributes . section : Introduction Cars represent a revolution in mobility and convenience , bringing us the flexibility of moving from place to place . The societal benefits ( and cost ) are far - reaching . Taillight ” wins among the different car parts , mostly likely due to the relatively more distinctive designs , and the model name printed close to the taillight , which is a very informative feature for the CNN model . We also combine predictions using the eight car part models by voting strategy . This strategy significantly improves the performance due to the complementary nature of different car parts . To decouple the influence of 2D detectors , we use ground truth 2D boxes for region proposals and use 3D box estimation accuracy ( IoU threshold 0.7 ) as the evaluation metric . We will only focus on the car category which has the most training examples . paragraph : Comparing with alternative approaches for 3D detection . In this section , we show that our 3D detection framework can also be extended to using bird ’s eye view proposals , which adds another orthogonal proposal source to achieve better overall 3D detection performance . We evaluate the results of car detection using LiDAR bird ’s eye view only proposals + point net ( Ours ( BV ) ) , and combine frustum point net and bird ’s Another field that has been transformed by this technology is Automatic Speech Recognition ( ASR ) . Thanks to modern Deep Neural Networks ( DNNs ) , current speech recognizers are now able to significantly outperform previous GMM - HMM systems , allowing ASR to be applied in several contexts , such as web - search , intelligent personal assistants , car control and radiological reporting . Despite the progress of the last decade , state - of - the - art speech recognizers are still far away from reaching satisfactory robustness and flexibility . Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics . A diverse set of ideas has been explored for this problem and established benchmark datasets are available . The intriguing area of self-driving car motivates us to build an autonomous driving platform . In this paper , we discuss the architecture of the self-driving car and its software components that include localization , detection , motion planning and mission planning . We also highlight the hardware modules that are responsible for controlling the car . Making a computer detect and understand traffic objects automatically be it vehicles , pedestrians or traffic lights are essential components for selfdriving cars or automated traffic analysis . This Ph.D. thesis addresses four computer vision topics in respect to traffic analysis , which are Video Acquisition , Object Detection , Semantic , and Knowledge for the World . These tasks for example detecting road lanes , pedestrians , or road signs . In this paper , as a step toward the goal of developing an autonomous self-driving car , we develop a prototype of autonomous line following robot . Using only visual information , we study a visual servoing technique for which the robot able to detect road lanes and navigate autonomously . In this paper we present an original contactless human machine interface for driving car . The proposed framework is based on the image sent by a simple camera device , which is then processed by various computer vision algorithms . The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future . The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system ( ITS ) . A robotic car , also known as a driverless or self-driving car , is an autonomous vehicle capable of sensing its environment and navigating itself without human input . Robotic cars exist mainly as prototypes and demonstration systems , but are likely to become more widespread in the near future . Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems , for example , robot navigation , driving a car , speech recognition , stock trading , and playing games . The downside of this generality is that exact algorithms are computationally intractable . Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car , traffic mapping and traffic surveillance . Recently , deep learning models demonstrated prominent representation capacity , and achieved outstanding performance in traffic sign recognition . In addition , a position correction method is presented by using a vehicle dynamics . Conventionally , selfdriving vehicle is operated by various expensive environmental recognition sensors . It results in rise in prices of the vehicle , and also the complicated system with various sensors tends to be a high possibility of malfunction . document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate . section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene . Many existing algorithms also make strong domain - specific assumptions . For example , MV3D assumes that all objects can be segmented in a top - down 2D view of the point cloud , which works for the common self - driving case but does not generalize to indoor scenes where objects can be placed on top of each other . Furthermore , the top - down view approach tends to work well for objects such as cars , but does not for other key object classes such as pedestrians or cyclists . In contrast , residential or urban driving environments feature a much higher degree of unpredictability with many generic objects , inconsistent lane - markings , and elaborate traffic flow patterns . The relative regularity and structure of highways has facilitated some of the first practical applications of autonomous driving technology . Many automakers have begun pursuing highway auto - pilot solutions designed to mitigate driver stress and fatigue and to provide additional safety features ; for example , certain advanced - driver assistance systems ( ADAS ) can both keep cars within their lane and perform front - view car detection . In this paper , we present a detection system capable of operating at greater than Hz using nothing but a laptop GPU . Due to the requirements of highway driving , we need to ensure that the system used can detect cars more than m away and can operate at speeds greater than Hz ; this distance requires higher image resolutions than is typically used , and in our case is . We use the Overfeat CNN detector , which is very scalable , and simulates a sliding window detector in a single forward pass in the network by efficiently reusing convolutional results on each layer . Pedestrian detection aims to predict bounding boxes of all the pedestrian instances in an image . It has attracted much attention within the computer vision community in recent years as an important component for many human - centric applications , such as self - driving vehicles , person re - identification , video surveillance and robotics . Recently , many research works have been devoted to pedestrian detection . subsubsection : KITTI The challenging KITTI dataset consists of 7 , 481 training and 7 , 518 test images , which are captured from an autonomous driving platform . Evaluation is done at three levels of difficulty : easy , moderate and hard , where the difficulty is measured by the minimal scale of the pedestrians to be considered and the occlusion and truncation of the pedestrians . Humans glance at an image and instantly know what objects are in the image , where they are , and how they interact . The human visual system is fast and accurate , allowing us to perform complex tasks like driving with little conscious thought . Fast , accurate algorithms for object detection would allow computers to drive cars without specialized sensors , enable assistive devices to convey real - time scene information to human users , and unlock the potential for general purpose , responsive robotic systems . The trained network is rather supposed to be generic and to work well in various scenarios . An additional optional component in dataset schedules is fine - tuning of a generic network to a specific scenario , such as the driving scenario , which we show in Section [ reference ] . section : Stacking Networks The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous . They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego-vehicle ’s movement . Over the last few years , the subject of autonomous vehicles has been attracting significant attention from researchers , as well as interest from the media and the public at large . It encompasses a myriad of new applications , ranging from driverless cars to airborne surveillance of sensitive areas . Designing solutions for autonomous vehicles requires the integration of hardware and software components , as well as interaction with cloud-based servers . Abstract Automatic detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles . In this paper , Deep Convolutional Neural Network ( CNN ) is used to develop an Autonomous Traffic and Road Sign ( ATRS ) detection and recognition system . In the future , “ driverless ” autonomous vehicles will replace traditional human-operated vehicles . However , we are still in the present where an average of eleven teenagers die every day in road accidents . Since the New York Times article was published , Google has added more features , the vehicles have ventured farther , and the prototypes have been tested by various audiences , including the blind . ( 6 ) Hoping to grab a head start in this nascent market and garner publicity , traditional car companies such as Toyota and Audi have joined the fray by developing driverless car prototypes and incorporating automated parking functions into existing cars . ( 7 ) Tesla has also contributed its own innovations , such as transforming traditional human-controlled vehicles to autonomous cars simply via software updates to the car 's onboard computers . For driverless driving cars , it is essential to detect drivable space . It can directly apply to plan driving paths by acquiring the occupancy grid map . Whilst autonomous driving is outside the scope of the Formula-SAE competition , it has been the subject of significant research interest over the last several decades . It is intended that the Autonomous SAE car developed in this project will provide UWA with a platform for research into driverless performance cars . This project consists of the design and implementation of a navigation control system which uses a Linux PC to interface with a range of sensors as well as the drive-by-wire system , safety systems and a base station . Development of driverless road vehicles is one of the most active research areas of robotics today . Path planning among obstacles is one of the challenging problems to be solved in order to achieve autonomous navigation . In many network engineering games , the performance metrics depend on some few aggregates of the parameters/choices . One typical example is the congestion field in traffic engineering where classical cars and smart autonomous driverless cars create traffic congestion levels on the roads . The congestion field can be learned , for example by means of crowdsensing , and can be used for efficient and accurate prediction of the end-to-end delays of commuters . Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections , but relatively little research has examined factors that contribute to accidents at railway-highway crossings . Between 1998 and 2002 in Korea , about 95 % of railway accidents occurred at highway-rail grade crossings , resulting in 402 accidents , of which about 20 % resulted in fatalities . Abstract Stress has been identified as one of the contributing factors to vehicle crashes which create a significant cost in terms of loss of life and productivity for governments and societies . Motivated by the need to address the significant costs of driver stress , it is essential to build a practical system that can detect drivers ’ stress levels in real time with high accuracy . Simulations are carried out and the results coincide well with those of real minibus crash test . The simulation and vehicle crash results show that : by using the finite element analysis for vehicle frontal crash simulation is effective . FINAL REPORT . The Roadside Safety Library ( RSL ) maintained by the Federal Highway Administration ( FHWA ) is a collection of computer programs which simulate vehicle handling , vehicular crash with roadside objects including safety appurtenances , and post-collision behavior of occupants , along with a collection of a large number of crash test films which serve to supplement and validate the simulation programs . Collectively , the simulation programs are called the Simulation Database . The objective of vehicle crash accident reconstruction is to investigate the pre-impact velocity . Elastic-plastic deformation of the vehicle and the collision objects are the important information produced during vehicle crash accidents , and the information can be fully utilized based on the finite element method ( FEM ) , which has been widely used as simulation tools for crashworthiness analyses and structural optimization design . However , the FEM is not becoming popular in accident reconstruction because it needs lots of crash simulation cycles and the FE models are getting bigger , which increases the simulation time and cost . Nowadays , People safe driving is one the greatest concerns of health authorities . Having mentioned to this issue , we must predict effective criterions in Vehicle crash injuries . In case of the crash , Belts usually can decrease damages unless a high speed crash occurs , Belts expose occupants to be hurt because of head rotation . The vehicle rear crash model with occupant restraint system was built and the research of occupant dynamic characteristic has been done in this paper . In order to improve ability of the seat belt protection , the fixing position of seat belt was optimized with reducing experiment times and cutting down experiment cost . Using Convolutional Neural Network . Fatigue driving is suspected to be a primary cause of vehicle related crashes . Driver fatigue resulting from lack of rest is a significant factor in the expanding number of the mishaps on roads . This paper has analyzed the basic theory and material constitutive relation of crashworthiness simulation . The finite element model for vehicle side crash has been built and the side crash simulation with an initial velocity of 50 km/h has been conducted . Though the structure deformations analysis of each part , the side crash impacts on passenger living space and battery have been predicted and the crashworthiness of the pure electric bus body structure has been tested . We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system . Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving . The CNN used for vehicle detection can be easily extended for lane boundary detection by adding an additional class . Whereas the regression for the vehicle class predicts a five dimensional value ( four for the bounding box and one for depth ) , the lane regression predicts six dimensions . Similar to the vehicle detector , the first four dimensions indicate the two end points of a local line segment of the lane boundary . Once the raw videos are collected , we annotate the 3D locations for vehicles and lanes as well as the relative speed of all the vehicles . To get vehicle annotations , we follow the conventional approach of using Amazon Mechanical Turk to get accurate bounding box locations within pixel space . Then , we match bounding boxes and radar returns to obtain the distance and relative speed of the vehicles .
The assumption of scene rigidity is typical in SLAM algorithms . Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments , which are the target of several relevant applications like service robotics or autonomous vehicles . In this letter we present DynaSLAM , a visual SLAM system that , building on ORB-SLAM2 , adds the capabilities of dynamic object detection and background inpainting .
We implemented autonomous driving vehicle system at an intersection equipped with traffic lights . This system was consisted of a traffic light recognition , crossing/stop decision algorithm , vehicle localization , vehicle longitudinal/lateral control , and coordinate map generation .
Now a days Autonomous electric vehicle are the current research trends in the automobile industry and future vehicle used by humans . However , the commercial realization of autonomous vehicles remains a significant challenge .
The illumination invariant colour space stems from modelling the spectral properties of the camera and scene illumination in conjunction , and requires only a single parameter derived from the image sensor specifications . We present results using a 24-hour dataset collected using an autonomous road vehicle , demonstrating increased consistency of the illumination invariant images in comparison to raw RGB images during daylight hours . We then present three example applications of how illumination invariant imaging can improve performance in the context of vision-based autonomous vehicles : 6-DoF metric localisation using monocular cameras over a 24-hour period , life-long visual localisation and mapping using stereo , and urban scene classification in changing environments .
This work serves as the proof of concept of an autonomous vehicle prototype developed by Moovita and Universiti Teknologi Malaysia . For a dependable driverless vehicle maneuver , it requires a stable velocity controller to allow for the desired longitudinal motion navigation .
Advanced Driver Assistance Systems ( ADAS ) are essential parts for developing the autonomous vehicle concept . They cooperate with different on-board car equipment to make driving safe and comfortable .
The system can make use of existing infrastructure , reduce urban sprawl and ease congestion . Autonomous vehicle based systems can improve fuel efficiency . The technology presents a window of opportunity for a new mode of transportation that obtains efficiencies of up to 0.25 l/100 km ( 1000 mpg equivalent ) , reducing U.S. petroleum consumption by up to 16 % .
In this paper , we outline a methodology for evaluating the situation awareness ( SA ) provided by a supervisory interface for an autonomous on-road vehicle . Our goal is to be able to use the evaluations to compare interface designs with respect to how well each facilitates the users ' acquisition of SA .
We present the new Road Event and Activity Detection ( READ ) dataset , designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving . READ will give scholars in computer vision , smart cars and machine learning at large the opportunity to conduct research into exciting new problems such as understanding complex ( road ) activities , discerning the behaviour of sentient agents , and predicting both the label and the location of future actions and events , with the final goal of supporting autonomous decision making .
Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible . Many real - world robotic systems are equipped with multiple 3D sensors : for example , autonomous cars often have multiple lidars and potentially also radars . Yet , current algorithms often assume a single RGB - D camera , which provides RGB - D images , or a single lidar sensor , which allows the creation of a local front view image of the lidar depth and intensity readings .
section : Introduction Since the DARPA Grand Challenges for autonomous vehicles , there has been an explosion in applications and research for self - driving cars . Among the different environments for self - driving cars , highway and urban roads are on opposite ends of the spectrum .
The principal concerns in road safety are understanding and preventing risky situations . A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents . Improving vehicle stabilization is possible when vehicle parameters are known .
The invention discloses an automobile safety protection device which is used for lowering damage degree of a car body in case of car crash . The automobile safety protection device is equipped with hydraulic columns and telescopic heads , wherein , top enclosures are sheathed on the front ends of the telescopic heads ; front springs are connected between the top enclosures and the telescopic heads ; locating slots are arranged inside the top enclosures ; the telescopic heads are equipped with bulges corresponding to the locating slots ; the front springs in the top enclosures are connected with bumpers ; air cushions are wrapped outside the bumpers ; gear sleeves are arranged outside the hydraulic columns and are connected with motor gears ; rear air bags are arranged at the tail parts of the hydraulic columns and are connected with tail springs ; and locating columns are also connected at the tail parts of the hydraulic columns .
The results show that the head - neck finite element model without muscle tissue has a consistent mechanics and dynamics with the body drop test . The model can be applied to the exploration of a more detailed neck injury model with muscle tissue and can also be directly applied to the study of injury mechanism of the cervical spine in the car crash accidents .
The car black box is a vehicle-based recorder which records various parameters like rotation of wheel , temperature of vehicle inside , battery voltage , seat belt and door lock observation . These data can be used for accurate car accident investigation and some public crimes prevention . Car black box is a digital electronic device which can record and store vehicle speed , in real-time and vehicle 's other status information .
In recent years , the importance of driver ’s support system is increasing as a solution for dealing with car related accidents . These driving support systems are equipped with functions for avoiding various hazards when the driver drives the vehicle , reducing the risk of causing an accident .
According to the statistics of car accident causes [ 11 ] given by Police of the Czech Republic , about 17.5 % of all car accidents are caused by lack of dedication to driving , including microsleep . A lot of car factories develop systems for fatigue prediction and microsleep detection .
Road safety telematic systems are complex heterogeneous systems . The technology to build them exists and can save lives and reduce the severity of car accident injuries . However for the solution to be feasible , among others , it is necessary to achieve multi-vendor interoperability .
The present invention relates to a central curtain airbag apparatus for a vehicle , which is used to protect the rear seat passenger airbag cushion during a car collision accident .
To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios . We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system .
This paper aims to highlight vision related tasks centered around “ car ” , which has been largely neglected by vision community in comparison to other objects . We show that there are still many interesting car - related problems and applications , which are not yet well explored and researched . To facilitate future car - related research , in this paper we present our on - going effort in collecting a large - scale dataset , “ CompCars ” , that covers not only different car views , but also their different internal and external parts , and rich attributes .
section : Introduction Cars represent a revolution in mobility and convenience , bringing us the flexibility of moving from place to place . The societal benefits ( and cost ) are far - reaching .
Taillight ” wins among the different car parts , mostly likely due to the relatively more distinctive designs , and the model name printed close to the taillight , which is a very informative feature for the CNN model . We also combine predictions using the eight car part models by voting strategy . This strategy significantly improves the performance due to the complementary nature of different car parts .
To decouple the influence of 2D detectors , we use ground truth 2D boxes for region proposals and use 3D box estimation accuracy ( IoU threshold 0.7 ) as the evaluation metric . We will only focus on the car category which has the most training examples . paragraph : Comparing with alternative approaches for 3D detection .
In this section , we show that our 3D detection framework can also be extended to using bird ’s eye view proposals , which adds another orthogonal proposal source to achieve better overall 3D detection performance . We evaluate the results of car detection using LiDAR bird ’s eye view only proposals + point net ( Ours ( BV ) ) , and combine frustum point net and bird ’s
Another field that has been transformed by this technology is Automatic Speech Recognition ( ASR ) . Thanks to modern Deep Neural Networks ( DNNs ) , current speech recognizers are now able to significantly outperform previous GMM - HMM systems , allowing ASR to be applied in several contexts , such as web - search , intelligent personal assistants , car control and radiological reporting . Despite the progress of the last decade , state - of - the - art speech recognizers are still far away from reaching satisfactory robustness and flexibility .
Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics . A diverse set of ideas has been explored for this problem and established benchmark datasets are available .
The intriguing area of self-driving car motivates us to build an autonomous driving platform . In this paper , we discuss the architecture of the self-driving car and its software components that include localization , detection , motion planning and mission planning . We also highlight the hardware modules that are responsible for controlling the car .
Making a computer detect and understand traffic objects automatically be it vehicles , pedestrians or traffic lights are essential components for selfdriving cars or automated traffic analysis . This Ph.D. thesis addresses four computer vision topics in respect to traffic analysis , which are Video Acquisition , Object Detection , Semantic , and Knowledge for the World .
These tasks for example detecting road lanes , pedestrians , or road signs . In this paper , as a step toward the goal of developing an autonomous self-driving car , we develop a prototype of autonomous line following robot . Using only visual information , we study a visual servoing technique for which the robot able to detect road lanes and navigate autonomously .
In this paper we present an original contactless human machine interface for driving car . The proposed framework is based on the image sent by a simple camera device , which is then processed by various computer vision algorithms .
The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future . The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system ( ITS ) .
A robotic car , also known as a driverless or self-driving car , is an autonomous vehicle capable of sensing its environment and navigating itself without human input . Robotic cars exist mainly as prototypes and demonstration systems , but are likely to become more widespread in the near future .
Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems , for example , robot navigation , driving a car , speech recognition , stock trading , and playing games . The downside of this generality is that exact algorithms are computationally intractable .
Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car , traffic mapping and traffic surveillance . Recently , deep learning models demonstrated prominent representation capacity , and achieved outstanding performance in traffic sign recognition .
In addition , a position correction method is presented by using a vehicle dynamics . Conventionally , selfdriving vehicle is operated by various expensive environmental recognition sensors . It results in rise in prices of the vehicle , and also the complicated system with various sensors tends to be a high possibility of malfunction .
document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate .
section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene .
Many existing algorithms also make strong domain - specific assumptions . For example , MV3D assumes that all objects can be segmented in a top - down 2D view of the point cloud , which works for the common self - driving case but does not generalize to indoor scenes where objects can be placed on top of each other . Furthermore , the top - down view approach tends to work well for objects such as cars , but does not for other key object classes such as pedestrians or cyclists .
In contrast , residential or urban driving environments feature a much higher degree of unpredictability with many generic objects , inconsistent lane - markings , and elaborate traffic flow patterns . The relative regularity and structure of highways has facilitated some of the first practical applications of autonomous driving technology . Many automakers have begun pursuing highway auto - pilot solutions designed to mitigate driver stress and fatigue and to provide additional safety features ; for example , certain advanced - driver assistance systems ( ADAS ) can both keep cars within their lane and perform front - view car detection .
In this paper , we present a detection system capable of operating at greater than Hz using nothing but a laptop GPU . Due to the requirements of highway driving , we need to ensure that the system used can detect cars more than m away and can operate at speeds greater than Hz ; this distance requires higher image resolutions than is typically used , and in our case is . We use the Overfeat CNN detector , which is very scalable , and simulates a sliding window detector in a single forward pass in the network by efficiently reusing convolutional results on each layer .
Pedestrian detection aims to predict bounding boxes of all the pedestrian instances in an image . It has attracted much attention within the computer vision community in recent years as an important component for many human - centric applications , such as self - driving vehicles , person re - identification , video surveillance and robotics . Recently , many research works have been devoted to pedestrian detection .
subsubsection : KITTI The challenging KITTI dataset consists of 7 , 481 training and 7 , 518 test images , which are captured from an autonomous driving platform . Evaluation is done at three levels of difficulty : easy , moderate and hard , where the difficulty is measured by the minimal scale of the pedestrians to be considered and the occlusion and truncation of the pedestrians .
Humans glance at an image and instantly know what objects are in the image , where they are , and how they interact . The human visual system is fast and accurate , allowing us to perform complex tasks like driving with little conscious thought . Fast , accurate algorithms for object detection would allow computers to drive cars without specialized sensors , enable assistive devices to convey real - time scene information to human users , and unlock the potential for general purpose , responsive robotic systems .
The trained network is rather supposed to be generic and to work well in various scenarios . An additional optional component in dataset schedules is fine - tuning of a generic network to a specific scenario , such as the driving scenario , which we show in Section [ reference ] . section : Stacking Networks
The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous . They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego-vehicle ’s movement .
Over the last few years , the subject of autonomous vehicles has been attracting significant attention from researchers , as well as interest from the media and the public at large . It encompasses a myriad of new applications , ranging from driverless cars to airborne surveillance of sensitive areas . Designing solutions for autonomous vehicles requires the integration of hardware and software components , as well as interaction with cloud-based servers .
Abstract Automatic detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles . In this paper , Deep Convolutional Neural Network ( CNN ) is used to develop an Autonomous Traffic and Road Sign ( ATRS ) detection and recognition system .
In the future , “ driverless ” autonomous vehicles will replace traditional human-operated vehicles . However , we are still in the present where an average of eleven teenagers die every day in road accidents .
Since the New York Times article was published , Google has added more features , the vehicles have ventured farther , and the prototypes have been tested by various audiences , including the blind . ( 6 ) Hoping to grab a head start in this nascent market and garner publicity , traditional car companies such as Toyota and Audi have joined the fray by developing driverless car prototypes and incorporating automated parking functions into existing cars . ( 7 ) Tesla has also contributed its own innovations , such as transforming traditional human-controlled vehicles to autonomous cars simply via software updates to the car 's onboard computers .
For driverless driving cars , it is essential to detect drivable space . It can directly apply to plan driving paths by acquiring the occupancy grid map .
Whilst autonomous driving is outside the scope of the Formula-SAE competition , it has been the subject of significant research interest over the last several decades . It is intended that the Autonomous SAE car developed in this project will provide UWA with a platform for research into driverless performance cars . This project consists of the design and implementation of a navigation control system which uses a Linux PC to interface with a range of sensors as well as the drive-by-wire system , safety systems and a base station .
Development of driverless road vehicles is one of the most active research areas of robotics today . Path planning among obstacles is one of the challenging problems to be solved in order to achieve autonomous navigation .
In many network engineering games , the performance metrics depend on some few aggregates of the parameters/choices . One typical example is the congestion field in traffic engineering where classical cars and smart autonomous driverless cars create traffic congestion levels on the roads . The congestion field can be learned , for example by means of crowdsensing , and can be used for efficient and accurate prediction of the end-to-end delays of commuters .
Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections , but relatively little research has examined factors that contribute to accidents at railway-highway crossings . Between 1998 and 2002 in Korea , about 95 % of railway accidents occurred at highway-rail grade crossings , resulting in 402 accidents , of which about 20 % resulted in fatalities .
Abstract Stress has been identified as one of the contributing factors to vehicle crashes which create a significant cost in terms of loss of life and productivity for governments and societies . Motivated by the need to address the significant costs of driver stress , it is essential to build a practical system that can detect drivers ’ stress levels in real time with high accuracy .
Simulations are carried out and the results coincide well with those of real minibus crash test . The simulation and vehicle crash results show that : by using the finite element analysis for vehicle frontal crash simulation is effective .
FINAL REPORT . The Roadside Safety Library ( RSL ) maintained by the Federal Highway Administration ( FHWA ) is a collection of computer programs which simulate vehicle handling , vehicular crash with roadside objects including safety appurtenances , and post-collision behavior of occupants , along with a collection of a large number of crash test films which serve to supplement and validate the simulation programs . Collectively , the simulation programs are called the Simulation Database .
The objective of vehicle crash accident reconstruction is to investigate the pre-impact velocity . Elastic-plastic deformation of the vehicle and the collision objects are the important information produced during vehicle crash accidents , and the information can be fully utilized based on the finite element method ( FEM ) , which has been widely used as simulation tools for crashworthiness analyses and structural optimization design . However , the FEM is not becoming popular in accident reconstruction because it needs lots of crash simulation cycles and the FE models are getting bigger , which increases the simulation time and cost .
Nowadays , People safe driving is one the greatest concerns of health authorities . Having mentioned to this issue , we must predict effective criterions in Vehicle crash injuries . In case of the crash , Belts usually can decrease damages unless a high speed crash occurs , Belts expose occupants to be hurt because of head rotation .
The vehicle rear crash model with occupant restraint system was built and the research of occupant dynamic characteristic has been done in this paper . In order to improve ability of the seat belt protection , the fixing position of seat belt was optimized with reducing experiment times and cutting down experiment cost .
Using Convolutional Neural Network . Fatigue driving is suspected to be a primary cause of vehicle related crashes . Driver fatigue resulting from lack of rest is a significant factor in the expanding number of the mishaps on roads .
This paper has analyzed the basic theory and material constitutive relation of crashworthiness simulation . The finite element model for vehicle side crash has been built and the side crash simulation with an initial velocity of 50 km/h has been conducted . Though the structure deformations analysis of each part , the side crash impacts on passenger living space and battery have been predicted and the crashworthiness of the pure electric bus body structure has been tested .
We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system . Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving .
The CNN used for vehicle detection can be easily extended for lane boundary detection by adding an additional class . Whereas the regression for the vehicle class predicts a five dimensional value ( four for the bounding box and one for depth ) , the lane regression predicts six dimensions . Similar to the vehicle detector , the first four dimensions indicate the two end points of a local line segment of the lane boundary .
Once the raw videos are collected , we annotate the 3D locations for vehicles and lanes as well as the relative speed of all the vehicles . To get vehicle annotations , we follow the conventional approach of using Amazon Mechanical Turk to get accurate bounding box locations within pixel space . Then , we match bounding boxes and radar returns to obtain the distance and relative speed of the vehicles .
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes Implementation of Autonomous Driving Vehicle at an Intersection with Traffic Light Recognition and Vehicle Controls OBSTACLE DETECTION AND AVOIDANCE FOR AUTONOMOUS ELECTRIC VEHICLE USING ARDUINO Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles Longitudinal Velocity Control Design with Error Tolerance Strategy for Autonomous Vehicle Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application Social ramifications of autonomous urban land vehicles Implementation of a situation awareness assessment tool for evaluation of human-robot interfaces Action Detection from a Robot-Car Perspective PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation An Empirical Evaluation of Deep Learning on Highway Driving Virtual sensors, application to vehicle tire-road normal forces for road safety Automobile safety protection device Development and Validation of a Finite Element Model of Human Cervical Spine Based on the Head-Neck Drop Test Design and Implementation of Car Black Box Based on ARM7 Effective feature extraction from driving data for detection of danger awareness Cognitive event-related potential waveform latency determination: Based on result of matching pursuit algorithm and Hilbert-Huang transform Test Tools for Road Safety Telematic Systems Center curtain airbag apparatus for a vehicle An Empirical Evaluation of Deep Learning on Highway Driving A large-scale car dataset for fine-grained categorization and verification Frustum PointNets for 3D Object Detection from RGB-D Data Light Gated Recurrent Units for Speech Recognition Taking a deeper look at pedestrians Autonomous Vehicle: The Architecture Aspect of Self Driving Car Building Brains for Visual Traffic Analysis Visual servoing line following robot: Camera-based line detecting and interpreting GPU Accelerated Contactless Human Machine Interface for Driving Car Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication Obstacle & Lane Detection and Local Path Planning for IGVC Robotic Vehicles Using Stereo Vision Policy-Gradient Algorithms for Partially Observable Markov Decision Processes Traffic sign recognition with convolutional neural network based on max pooling positions Multiple Vehicles Semi-Self-driving System Using GNSS Coordinate Tracking under Relative Position with Correction Algorithm Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation An Empirical Evaluation of Deep Learning on Highway Driving Scale-Aware Fast R-CNN for Pedestrian Detection You Only Look Once: Unified, Real-Time Object Detection FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding Autonomous Vehicles and Mechatronics Education Autonomous Traffic Sign (ATSR) Detection and Recognition using Deep CNN Feasibility Study on the Utilization of Microsoft HoloLens to Increase Driving Conditions Awareness Grabbing the Wheel Early: Moving Forward on Cybersecurity and Privacy Protections for Driverless Cars Drivable space expansion from the ground base for complex structured roads Development of a Navigation Control System for an Autonomous Formula SAE-Electric Race Car Effective navigation in narrow areas: A planning method for autonomous cars Mean-Field-Type Games in Engineering Accident prediction model for railway-highway interfaces. Automatic driver stress level classification using multimodal deep learning Minibus Frontal Crash Analysis Based on LS-DYNA ROADSIDE SAFETY LIBRARY. LIBRARY INFORMATION RETRIEVAL SYSTEM (LIRS). VOLUME 2: PROGRAMMERS' AND OPERATORS' MANUAL. FINAL REPORT Vehicle crash accident reconstruction based on the analysis 3D deformation of the auto-body The biomechanical investigation of cervical spine injury in case of Vehicle crash using finite-element approach Improvement of Seat Belt Protection Ability for Vehicle Rear Crash Driver Fatigue Detection Based On Eye State Recognition Using Convolutional Neural Network Pure Electric Bus Crashworthiness Analysis An Empirical Evaluation of Deep Learning on Highway Driving
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VEHITS ICCCI (2) ISTAS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ACC iCAST BMEI VTC Spring ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE Transactions on Emerging Topics in Computational Intelligence 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) RiTA ICNC-FSKD 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv IEEE Transactions on Multimedia 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SMC ArXiv
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