Globally localizing information , such as a GPS signal , can be unreliable due to signal shadowing and multipath errors . Detailed a priori maps of the environment with sufficient information for autonomous navigation typically require driving the area multiple times to collect large amounts of data , substantial post-processing on that data to obtain the map , and then maintaining updates on the map as the environment changes . This paper addresses the issue of autonomous driving in an urban environment by investigating algorithms and an architecture to enable fully functional autonomous driving with limited information . This thesis explores different aspects of the autonomous navigation problem for vehicles equipped with cameras and inertial measurement units . First , we propose a novel camera model for wide field-of-view cameras and two direct methods for visual-inertial odometry . This paper deals with the autonomous navigation problem . Its objective is the driving of a mobile robot to the goal position without colliding with obstacles . This paper describes an autonomous platform for navigation and surveying within networks of tunnels , as those typically found in underground mines and caves . In this context , we propose a system allowing two modes of operation : surveying and autonomous navigation mode . In the surveying mode , a remotely located supervisor instructs the platform to move through successive sections of the network , gathering range data that is then concatenated into two- and three-dimensional survey maps of the environment . When the navigation system is operating in the normal mode , this reference is maintained by the ground and measured through the satellite – ground link and modifies the onboard clock parameters to ensure the uniformity of the entire system time reference . In the autonomous navigation mode , the navigation constellation to leave the ground support , need to maintain the time reference autonomously . This paper analyzes the maintenance method of autonomous time reference based on inter-satellite observation and designs a distributed autonomous time reference maintenance algorithm . In this paper , we use an existing prototype consisting of a head-mounted display with an external camera to investigate the impact of a third person view regarding user experience and the evaluation of viewing angles . Besides manual view positioning by a user , we used an autonomic navigation task to compare both approaches . Results show that autonomic positioning shows significantly less errors than a manual positioning of the camera . Even though , laser based scanning solutions for Simultaneous Location And Mapping ( SLAM ) is considered as the most reliable for depth estimation , they are not feasible for use in UAV and land-based small vehicles due to their physical size and weight . Stereovision is considered as the best approach for any autonomous navigation solution as stereo rigs are considered to be lightweight and inexpensive . However , stereoscopy which estimates the depth information through pairs of stereo images can still be computationally expensive and unreliable . Object size identification is very useful in building systems or applications especially in autonomous system navigation . Many recent works have started to use multiple vision sensors or cameras for different type of application such as 3D image constructions , occlusion detection and etc . This paper briefly describes the hardand software system of TAS/UniBw ’s entry for the 2009 C-ELROB robotic trials , participating at the autonomous navigation scenario . At the core of the system is the so-called tentacles approach to reactive robot navigation , that we already demonstrated successfully at the 2007 C-ELROB trials . A greater degree of survivability is a significant requirement of future space systems . Toward this end , an important aspect is an autonomous navigation capability . For many space systems , an obvious choice is the use of the Navstar/Global Positioning System ( GPS ) . After one complete flight , with the collected data the historic UAV path and the indoor environment can be well estimated . The autonomous navigation part of the system does not require any remote sensory information or off-line computational power , while the mapping is done off-line . Complete flight tests have been carried out to verify fidelity and performance the navigation solution . In robotic field , autonomous navigation safely is a timely subject . Therefore , many researchers try to elaborate a reliable system to respond this exigency . And a new descriptor named Second-order Gradient for Pedestrian detection ( G2P ) is presented . Experimental results demonstrate the advantage of the G2P descriptor over the standard HOG descriptor with ETH , CVC-02-system , NITCA and KITTI dataset , which also reflects the effectiveness of the DGM-based PDEF in finding better descriptors for pedestrian detection . A method is presented for pedestrian detection using a stereo night-vision system installed on the vehicle . Much of the work in this area makes use of shape information . Indoor positioning systems are required for many new applications and , ideally , should provide high accuracy at zero costs for initial setup , maintenance and per user . Many approaches thus use an existing Wi-Fi infrastructure and smart-phones for pedestrian location estimation . While the well-known Wi-Fi fingerprinting provides good localization accuracy down to one meter , necessary time and costs are tremendous . Solving this task is required in a broad range of applications , such as distributed sensor networks and radar-based person identification . For this purpose , an adaptive joint object labeling and tracking algorithm is proposed and applied to a real data use case of pedestrian labeling in a calibration-free multi-object multi-camera setup with low video resolution and frequent object occlusions . The proposed algorithm is well suited for ad hoc networks , as it requires neither registration of camera views nor a fusion center . Pedestrian density estimation is one of the key problems in intelligent transportation systems and has been widely applied to a number of applications in other fields of engineering . Counting-by-regression methods are more favorable for coping with such a problem owing to their robustness against interperson occlusion and relaxing the impractical requirement of a high video frame rate , compared to counting-by-detection and counting-by-clustering methods . For pedestrian velocity extraction , the framework incorporates cross-correlation based Particle Image Velocimetry ( PIV ) techniques . For pedestrian density estimation , the framework relies on the Machine Learning technique of the Boosted Regression Trees . The information collected from images in pixel coordinates are transformed to world coordinates with a pin-hole camera based projective transformation technique . The pedestrian detection literature has been recently extended by the availability of large-scale multisensory datasets , able to capture complementary aspects of the objects of interest , namely , appearance , motion , and depth . In this paper , we exploit this multimodal scenario to propose a new set of composite descriptors dubbed CO2 , CO-variances of visual features and CO-occurrences of depth fields . This paper describes an innovative , spatially-disaggregate framework to integrate pedestrian demand estimation with trip-based travel models : MoPeD , a model of pedestrian demand . Designed for the Portland , Oregon , region , the proposed method applies trip generation to new 264 ft by 264 ft ( 80 m by 80 m ) gridded pedestrian analysis zones ( PAZs ) . Statement of Financial Interest The authors have no financial interest in and will receive no financial gain from promulgating or distributing the concepts and innovations contained in this submission . Statement of Innovation This research brief describes an innovative , spatially-disaggregate framework to integrate pedestrian demand estimation with trip-based travel demand models . It proposes a new zonal structure , environment measure , and method to analyze pedestrian travel . Therefore , to achieve an effective estimation of pedestrian count , principal component analysis is used to reduce the dimension of the global representation features , and a fitting model between image global features and pedestrian count is constructed via support vector regression . The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world . However , most passages start and finish with ' walking ' , regardless of the kinds of transportation . It is judged that although traffic environments are easy to assess due to the criteria , which are relatively simple to digitize , such as velocity , density and congestion , it is hard to analyze pedestrian density using mechanical measurements , since pedestrian density analysis is a kind of environmental service assessment . This study aims to analyze pedestrian density at a specific pedestrian passage , count the number of pedestrians and measure how pedestrian behaviors are distributed , by simplifying this pedestrian density analysis method and suggesting an easier way of analyzing . Pedestrian quantity estimation receives increasing attention and has important applications , e.g. in location evaluation and risk analysis . In this work , we focus on pedestrian quantity estimation for event monitoring . We address the problem ( 1 ) how to estimate quantities for unmeasured locations , and ( 2 ) where to place a bounded number of sensors during different phases of a soccer match . This paper presents an indoor area occupancy counting system utilizing the ambient structural vibration induced by pedestrian footsteps . Our system achieves 99.55 % accuracy in pedestrian footsteps detection , 0.2 people mean estimation error in pedestrian traffic estimation , and 0.2 area occupant activity estimation error in real-world uncontrolled experiments . Local Decorrelation For Improved Pedestrian Detection Even with the advent of more sophisticated , data - hungry methods , boosted decision trees remain extraordinarily successful for fast rigid object detection , achieving top accuracy on numerous datasets . While effective , most boosted detectors use decision trees with orthogonal ( single feature ) splits , and the topology of the resulting decision boundary may not be well matched to the natural topology of the data . ∗This research was performed while W.N. was a postdoctoral researcher at POSTECH . We evaluate boosted decision tree learning with decorrelated features in the context of pedestrian detection . As our baseline we utilize the aggregated channel features ( ACF ) detector [ 7 ] , a popular , top - performing detector for which source code is available online . Convolutional neural networks ( CNN ) date back decades [ reference ] and deep CNNs have recently shown an explosive popularity partially due to its success in image classification [ reference ] , [ reference ] . They have also been successfully applied to other computer vision fields , such as object detection [ reference ] , [ reference ] , [ reference ] , face recognition [ reference ] , and pedestrian detection [ reference ] . Several factors are of central importance in this progress : ( i ) the efficient training implementation on modern powerful GPUs [ reference ] , ( ii ) the proposal of the Rectified Linear Unit ( ReLU ) [ reference ] which makes convergence much faster while still presents good quality [ reference ] , and ( iii ) the easy access to an abundance of data ( like ImageNet [ reference ] ) for training larger models . Angelova et al . [ ] combine the ideas of fast cascade and a deep network to detect pedestrian . Yang et al . [ ] introduce a novel bounding box regression loss to detect pedestrians in the crowd scenes . Although numerous pedestrian detection methods are presented in literature , how to robustly detect each individual pedestrian in crowded scenarios is still one of the most critical issues for pedestrian detectors . section : Occlusion - aware R - CNN document : Scale - aware Fast R - CNN for Pedestrian Detection In this work , we consider the problem of pedestrian detection in natural scenes . Intuitively , instances of pedestrians with different spatial scales may exhibit dramatically different features . The current objective of the program is to develop an Autonomous Off-Road Unmanned Ground Vehicle ( UGV ) . The intention is to deal through this project with some of the most urgent challenges in the field of autonomous vehicles . One of these challenges , is the lack of efficient Safety Performance Verification technique , as the existing tools for hardware and software reliability and safety engineering do not provide a comprehensive solution regarding algorithms that are based on Artificial Intelligent ( AI ) and Machine Learning . This paper presents a novel approach for learning self-awareness models for autonomous vehicles . Proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator . Then , based on the safety model , the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt . Finally , based on the improved artificial potential field , a collision-free path for autonomous driving vehicles is generated . To verify the performance of the proposed algorithm , co-simulation and real vehicle tests are carried out . Solving tasks for autonomous road vehicles using computer vision is a dynamic and active research field . However , one aspect of autonomous transportation has received little contributions : the rail domain . One of the main problems in autonomous electric vehicles is the energy storage , because a high autonomy and high power condition demand large mass , big volume and high cost of the storage unit . Consequently , in order to avoid power losses and to downsize the storage unit and the electric systems , the electric power train in the vehicle must be as efficient as possible . subsection : The Effects of Low - Resolution Currently there is need for head pose estimation at a distance and there exist multiple example applications in areas such as video surveillance , autonomous driving and advertisement . Future head pose estimation methods should look to improve estimation for low - resolution heads . Speed and distraction are among the top contributing factors to work zone crashes ( Garber & Zhao , 2002 ; Mountain , Hirst , & Maher , 2005 ; Wilson , Willis , Hendrikz , Le Brocque , & Bellamy , 2006 ) . The purpose of this study was to investigate the impact of different types of speed enforcement methods on driver attention in work zones . The investigation not only examined enforcement methods currently used in Minnesota , but also examined how implementing automated speed enforcement ( ASE ) , which is not currently used in Minnesota , may influence driver attention and behavior in simulated work zones . Two new algorithms are introduced that require either two or three feature points to be extracted from each image . Our algorithms are robust and run in real-time on a typical PC , which makes our system useful for a large variety of needs , from driver attention monitoring to machine-human interaction . We conducted a driving simulation study to assess the effect of distance guide sign format on visual attention allocation , target detection accuracy , and driving performance considering driver demographics . Results revealed distance guide sign format , including random or distance-ordered presentation of destinations , to have no impact on driver visual attention , target identification , and vehicle control . However , elderly drivers had difficulty in identifying targets when destinations were presented in random order . More than 90 % of the persistently increasing traffic fatalities is related to human choice/error . Monitoring driver attention has a direct effect on decreasing injury/fatality rates . In recent years , there has been much effort to estimate drivers ’ state with the goal of improving their driving behavior and preventing vehicle crashes in the first place . Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent . The indication of which elements in the scene are likely to capture the driver 's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis . This requires advanced instrumentation of the vehicle , and subsequent data reduction , analysis and interpretation are demanding . In conclusion , driver attention assessment in real traffic is a complex task , but a combination of visual occlusion , eye tracking and thinking aloud is a promising combination of methods to come further on the way . In the current driving simulator experiment , two different alerting devices , presented to the audio and tactile sense modality respectively , were used to study the effects on driver attention control . The experiment was carried out at VTI , ( Swedish Road and Transport Research Institute ) . The accuracy of head pose estimation is significant for many computer vision applications such as face recognition , driver attention detection and human-computer interaction . Most appearance-based head pose estimation works typically extract the low-dimensional face appearance features in some statistic subspaces , where the subspaces represent the underlying geometry structure of the pose space . Further the system is able to track through yawning , which is a large local mouth motion . Finally , results are presented , and future work on how this system can be used for more advanced driver visual attention monitoring is discussed .
Globally localizing information , such as a GPS signal , can be unreliable due to signal shadowing and multipath errors . Detailed a priori maps of the environment with sufficient information for autonomous navigation typically require driving the area multiple times to collect large amounts of data , substantial post-processing on that data to obtain the map , and then maintaining updates on the map as the environment changes . This paper addresses the issue of autonomous driving in an urban environment by investigating algorithms and an architecture to enable fully functional autonomous driving with limited information .
This thesis explores different aspects of the autonomous navigation problem for vehicles equipped with cameras and inertial measurement units . First , we propose a novel camera model for wide field-of-view cameras and two direct methods for visual-inertial odometry .
This paper deals with the autonomous navigation problem . Its objective is the driving of a mobile robot to the goal position without colliding with obstacles .
This paper describes an autonomous platform for navigation and surveying within networks of tunnels , as those typically found in underground mines and caves . In this context , we propose a system allowing two modes of operation : surveying and autonomous navigation mode . In the surveying mode , a remotely located supervisor instructs the platform to move through successive sections of the network , gathering range data that is then concatenated into two- and three-dimensional survey maps of the environment .
When the navigation system is operating in the normal mode , this reference is maintained by the ground and measured through the satellite – ground link and modifies the onboard clock parameters to ensure the uniformity of the entire system time reference . In the autonomous navigation mode , the navigation constellation to leave the ground support , need to maintain the time reference autonomously . This paper analyzes the maintenance method of autonomous time reference based on inter-satellite observation and designs a distributed autonomous time reference maintenance algorithm .
In this paper , we use an existing prototype consisting of a head-mounted display with an external camera to investigate the impact of a third person view regarding user experience and the evaluation of viewing angles . Besides manual view positioning by a user , we used an autonomic navigation task to compare both approaches . Results show that autonomic positioning shows significantly less errors than a manual positioning of the camera .
Even though , laser based scanning solutions for Simultaneous Location And Mapping ( SLAM ) is considered as the most reliable for depth estimation , they are not feasible for use in UAV and land-based small vehicles due to their physical size and weight . Stereovision is considered as the best approach for any autonomous navigation solution as stereo rigs are considered to be lightweight and inexpensive . However , stereoscopy which estimates the depth information through pairs of stereo images can still be computationally expensive and unreliable .
Object size identification is very useful in building systems or applications especially in autonomous system navigation . Many recent works have started to use multiple vision sensors or cameras for different type of application such as 3D image constructions , occlusion detection and etc .
This paper briefly describes the hardand software system of TAS/UniBw ’s entry for the 2009 C-ELROB robotic trials , participating at the autonomous navigation scenario . At the core of the system is the so-called tentacles approach to reactive robot navigation , that we already demonstrated successfully at the 2007 C-ELROB trials .
A greater degree of survivability is a significant requirement of future space systems . Toward this end , an important aspect is an autonomous navigation capability . For many space systems , an obvious choice is the use of the Navstar/Global Positioning System ( GPS ) .
After one complete flight , with the collected data the historic UAV path and the indoor environment can be well estimated . The autonomous navigation part of the system does not require any remote sensory information or off-line computational power , while the mapping is done off-line . Complete flight tests have been carried out to verify fidelity and performance the navigation solution .
In robotic field , autonomous navigation safely is a timely subject . Therefore , many researchers try to elaborate a reliable system to respond this exigency .
And a new descriptor named Second-order Gradient for Pedestrian detection ( G2P ) is presented . Experimental results demonstrate the advantage of the G2P descriptor over the standard HOG descriptor with ETH , CVC-02-system , NITCA and KITTI dataset , which also reflects the effectiveness of the DGM-based PDEF in finding better descriptors for pedestrian detection .
A method is presented for pedestrian detection using a stereo night-vision system installed on the vehicle . Much of the work in this area makes use of shape information .
Indoor positioning systems are required for many new applications and , ideally , should provide high accuracy at zero costs for initial setup , maintenance and per user . Many approaches thus use an existing Wi-Fi infrastructure and smart-phones for pedestrian location estimation . While the well-known Wi-Fi fingerprinting provides good localization accuracy down to one meter , necessary time and costs are tremendous .
Solving this task is required in a broad range of applications , such as distributed sensor networks and radar-based person identification . For this purpose , an adaptive joint object labeling and tracking algorithm is proposed and applied to a real data use case of pedestrian labeling in a calibration-free multi-object multi-camera setup with low video resolution and frequent object occlusions . The proposed algorithm is well suited for ad hoc networks , as it requires neither registration of camera views nor a fusion center .
Pedestrian density estimation is one of the key problems in intelligent transportation systems and has been widely applied to a number of applications in other fields of engineering . Counting-by-regression methods are more favorable for coping with such a problem owing to their robustness against interperson occlusion and relaxing the impractical requirement of a high video frame rate , compared to counting-by-detection and counting-by-clustering methods .
For pedestrian velocity extraction , the framework incorporates cross-correlation based Particle Image Velocimetry ( PIV ) techniques . For pedestrian density estimation , the framework relies on the Machine Learning technique of the Boosted Regression Trees . The information collected from images in pixel coordinates are transformed to world coordinates with a pin-hole camera based projective transformation technique .
The pedestrian detection literature has been recently extended by the availability of large-scale multisensory datasets , able to capture complementary aspects of the objects of interest , namely , appearance , motion , and depth . In this paper , we exploit this multimodal scenario to propose a new set of composite descriptors dubbed CO2 , CO-variances of visual features and CO-occurrences of depth fields .
This paper describes an innovative , spatially-disaggregate framework to integrate pedestrian demand estimation with trip-based travel models : MoPeD , a model of pedestrian demand . Designed for the Portland , Oregon , region , the proposed method applies trip generation to new 264 ft by 264 ft ( 80 m by 80 m ) gridded pedestrian analysis zones ( PAZs ) .
Statement of Financial Interest The authors have no financial interest in and will receive no financial gain from promulgating or distributing the concepts and innovations contained in this submission . Statement of Innovation This research brief describes an innovative , spatially-disaggregate framework to integrate pedestrian demand estimation with trip-based travel demand models . It proposes a new zonal structure , environment measure , and method to analyze pedestrian travel .
Therefore , to achieve an effective estimation of pedestrian count , principal component analysis is used to reduce the dimension of the global representation features , and a fitting model between image global features and pedestrian count is constructed via support vector regression . The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world .
However , most passages start and finish with ' walking ' , regardless of the kinds of transportation . It is judged that although traffic environments are easy to assess due to the criteria , which are relatively simple to digitize , such as velocity , density and congestion , it is hard to analyze pedestrian density using mechanical measurements , since pedestrian density analysis is a kind of environmental service assessment . This study aims to analyze pedestrian density at a specific pedestrian passage , count the number of pedestrians and measure how pedestrian behaviors are distributed , by simplifying this pedestrian density analysis method and suggesting an easier way of analyzing .
Pedestrian quantity estimation receives increasing attention and has important applications , e.g. in location evaluation and risk analysis . In this work , we focus on pedestrian quantity estimation for event monitoring . We address the problem ( 1 ) how to estimate quantities for unmeasured locations , and ( 2 ) where to place a bounded number of sensors during different phases of a soccer match .
This paper presents an indoor area occupancy counting system utilizing the ambient structural vibration induced by pedestrian footsteps . Our system achieves 99.55 % accuracy in pedestrian footsteps detection , 0.2 people mean estimation error in pedestrian traffic estimation , and 0.2 area occupant activity estimation error in real-world uncontrolled experiments .
Local Decorrelation For Improved Pedestrian Detection Even with the advent of more sophisticated , data - hungry methods , boosted decision trees remain extraordinarily successful for fast rigid object detection , achieving top accuracy on numerous datasets . While effective , most boosted detectors use decision trees with orthogonal ( single feature ) splits , and the topology of the resulting decision boundary may not be well matched to the natural topology of the data .
∗This research was performed while W.N. was a postdoctoral researcher at POSTECH . We evaluate boosted decision tree learning with decorrelated features in the context of pedestrian detection . As our baseline we utilize the aggregated channel features ( ACF ) detector [ 7 ] , a popular , top - performing detector for which source code is available online .
Convolutional neural networks ( CNN ) date back decades [ reference ] and deep CNNs have recently shown an explosive popularity partially due to its success in image classification [ reference ] , [ reference ] . They have also been successfully applied to other computer vision fields , such as object detection [ reference ] , [ reference ] , [ reference ] , face recognition [ reference ] , and pedestrian detection [ reference ] . Several factors are of central importance in this progress : ( i ) the efficient training implementation on modern powerful GPUs [ reference ] , ( ii ) the proposal of the Rectified Linear Unit ( ReLU ) [ reference ] which makes convergence much faster while still presents good quality [ reference ] , and ( iii ) the easy access to an abundance of data ( like ImageNet [ reference ] ) for training larger models .
Angelova et al . [ ] combine the ideas of fast cascade and a deep network to detect pedestrian . Yang et al .
[ ] introduce a novel bounding box regression loss to detect pedestrians in the crowd scenes . Although numerous pedestrian detection methods are presented in literature , how to robustly detect each individual pedestrian in crowded scenarios is still one of the most critical issues for pedestrian detectors . section : Occlusion - aware R - CNN
document : Scale - aware Fast R - CNN for Pedestrian Detection In this work , we consider the problem of pedestrian detection in natural scenes . Intuitively , instances of pedestrians with different spatial scales may exhibit dramatically different features .
The current objective of the program is to develop an Autonomous Off-Road Unmanned Ground Vehicle ( UGV ) . The intention is to deal through this project with some of the most urgent challenges in the field of autonomous vehicles . One of these challenges , is the lack of efficient Safety Performance Verification technique , as the existing tools for hardware and software reliability and safety engineering do not provide a comprehensive solution regarding algorithms that are based on Artificial Intelligent ( AI ) and Machine Learning .
This paper presents a novel approach for learning self-awareness models for autonomous vehicles . Proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator .
Then , based on the safety model , the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt . Finally , based on the improved artificial potential field , a collision-free path for autonomous driving vehicles is generated . To verify the performance of the proposed algorithm , co-simulation and real vehicle tests are carried out .
Solving tasks for autonomous road vehicles using computer vision is a dynamic and active research field . However , one aspect of autonomous transportation has received little contributions : the rail domain .
One of the main problems in autonomous electric vehicles is the energy storage , because a high autonomy and high power condition demand large mass , big volume and high cost of the storage unit . Consequently , in order to avoid power losses and to downsize the storage unit and the electric systems , the electric power train in the vehicle must be as efficient as possible .
subsection : The Effects of Low - Resolution Currently there is need for head pose estimation at a distance and there exist multiple example applications in areas such as video surveillance , autonomous driving and advertisement . Future head pose estimation methods should look to improve estimation for low - resolution heads .
Speed and distraction are among the top contributing factors to work zone crashes ( Garber & Zhao , 2002 ; Mountain , Hirst , & Maher , 2005 ; Wilson , Willis , Hendrikz , Le Brocque , & Bellamy , 2006 ) . The purpose of this study was to investigate the impact of different types of speed enforcement methods on driver attention in work zones . The investigation not only examined enforcement methods currently used in Minnesota , but also examined how implementing automated speed enforcement ( ASE ) , which is not currently used in Minnesota , may influence driver attention and behavior in simulated work zones .
Two new algorithms are introduced that require either two or three feature points to be extracted from each image . Our algorithms are robust and run in real-time on a typical PC , which makes our system useful for a large variety of needs , from driver attention monitoring to machine-human interaction .
We conducted a driving simulation study to assess the effect of distance guide sign format on visual attention allocation , target detection accuracy , and driving performance considering driver demographics . Results revealed distance guide sign format , including random or distance-ordered presentation of destinations , to have no impact on driver visual attention , target identification , and vehicle control . However , elderly drivers had difficulty in identifying targets when destinations were presented in random order .
More than 90 % of the persistently increasing traffic fatalities is related to human choice/error . Monitoring driver attention has a direct effect on decreasing injury/fatality rates . In recent years , there has been much effort to estimate drivers ’ state with the goal of improving their driving behavior and preventing vehicle crashes in the first place .
Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent . The indication of which elements in the scene are likely to capture the driver 's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis .
This requires advanced instrumentation of the vehicle , and subsequent data reduction , analysis and interpretation are demanding . In conclusion , driver attention assessment in real traffic is a complex task , but a combination of visual occlusion , eye tracking and thinking aloud is a promising combination of methods to come further on the way .
In the current driving simulator experiment , two different alerting devices , presented to the audio and tactile sense modality respectively , were used to study the effects on driver attention control . The experiment was carried out at VTI , ( Swedish Road and Transport Research Institute ) .
The accuracy of head pose estimation is significant for many computer vision applications such as face recognition , driver attention detection and human-computer interaction . Most appearance-based head pose estimation works typically extract the low-dimensional face appearance features in some statistic subspaces , where the subspaces represent the underlying geometry structure of the pose space .
Further the system is able to track through yawning , which is a large local mouth motion . Finally , results are presented , and future work on how this system can be used for more advanced driver visual attention monitoring is discussed .
Autonomous Urban Localization and Navigation with Limited Information Visual-Inertial Navigation for Autonomous Vehicles A hybrid fuzzy-sliding mode controller for a mobile robot Autonomous system for navigation and surveying in underground mines Research on Distributed Autonomous Time Reference Maintain Method of Navigation Constellation Exploring the Optimal Point of View in Third Person Out-of-Body Experiences Stereo Correspondence Using Moment Invariants Object Distance and Size Measurement Using Stereo Vision System Team MuCAR-3 at C-ELROB 2009 NAVIGATING LOW ALTITUDE SATELLITES USING THE CURRENT FOUR NAVSTAR/GPS SATELLITES A Comprehensive UAV Indoor Navigation System Based on Vision Optical Flow and Laser FastSLAM Multi Sensor Data Fusion With Risk Assessment G2P: a new descriptor for pedestrian detection Pedestrian detection using stereo night vision Robust self-localization using Wi-Fi, step/turn-detection and recursive density estimation Robust and Distributed Cluster Enumeration and Object Labeling Pedestrian Density Analysis in Public Scenes With Spatiotemporal Tensor Features Towards Real-Time Monitoring of the Hajj Stereo-Based Framework for Pedestrian Detection with Partial Occlusion Handling Introducing MoPeD 2.0: A Model of Pedestrian Demand, Integrated with Trip-Based Travel Demand Forecasting Models Pedestrian count estimation using texture feature with spatial distribution 철도여객시설의 보행밀도 분석에 관한 연구 Incorporating Mobility Patterns in Pedestrian Quantity Estimation and Sensor Placement Area Occupancy Counting Through Sparse Structural Vibration Sensing Local Decorrelation For Improved Pedestrian Detection Image Super-Resolution Using Deep Convolutional Networks Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd Scale-Aware Fast R-CNN for Pedestrian Detection RobIL — Israeli program for research and development of autonomous UGV: Performance evaluation methodology Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm RailSem19: A Dataset for Semantic Rail Scene Understanding Efficiency optimization of a single-phase boost DC-DC converter for electric vehicle applications Fine-Grained Head Pose Estimation Without Keypoints Examining the Impact of ASE (Automated Speed Enforcement) in Work Zones on Driver Attention Single image face orientation and gaze detection Effect of Driver Age and Distance Guide Sign Format on Driver Attention Allocation and Performance EEG Based Driver Inattention Identification via Feature Profiling and Dimensionality Reduction Predicting the Driver's Focus of Attention: The DR(eye)VE Project Evaluation of methods for the assessment of attention while driving. Using the Cocktail Party Effect as Driver Attention Control An effective head pose estimation approach using Lie Algebrized Gaussians based face representation Determining driver visual attention with one camera
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Intelligent Vehicles Symposium PETRA ICIC (3) IPIN AVSS CitiSens NIPS IEEE Transactions on Pattern Analysis and Machine Intelligence ECCV IEEE Transactions on Multimedia CVPR Workshops IECON 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Advances in Body Area Networks I
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