A behavior planner for a vehicle generates a plurality of conditional action sequences of the vehicle using a tree search algorithm and heuristics obtained from one or more machine learning models. Each sequence corresponds to a sequence of anticipated states of the vehicle. At least some of the action sequences are provided to a motion selector of the vehicle. The motion selector generates motion-control directives based on the received conditional action sequences and on data received from one or more sensors of the vehicle, and transmits the directives to control subsystems of the vehicle.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a behavior planner implemented using one or more computing devices; and a motion selector of a vehicle, wherein the vehicle comprises one or more motion control subsystems; wherein the behavior planner is configured to: generate, using a combination of (a) a Monte Carlo Tree Search (MCTS) algorithm and (b) heuristics obtained from one or more machine learning models for the MCTS algorithm, a plurality of conditional action sequences corresponding to the vehicle, including a first conditional action sequence and a second conditional action sequence, wherein the first conditional action sequence corresponds to a first set of anticipated states of the vehicle, and wherein the second conditional action sequence corresponds to a second set of anticipated states of the vehicle; provide, at a first transmission rate, at least some conditional action sequences of the plurality of conditional action sequences to the motion selector; and wherein the motion selector is configured to: generate, based at least in part on (a) one or more conditional action sequences provided by the behavior planner and (b) sensor data obtained at a second transmission rate, one or more motion-control directives, wherein the second rate exceeds the first rate; and transmit the one or more motion-control directives to the one or more motion control subsystems. 2. The system as recited in claim 1, wherein the one or more machine learning models include (a) a first neural network model which provides a heuristic to identify, at a particular level of a particular tree, a subset of nodes to be included in a search, and (b) a second neural network model which provides a heuristic to limit a depth of the search within the particular tree. 3. The system as recited in claim 1, wherein to generate the plurality of conditional action sequences, the behavior planner is configured to: utilize a statistical distribution of a particular set of actions taken by one or more entities under a particular set of driving conditions, wherein the first conditional action sequence includes a proposed implementation of a first action of the particular set, and wherein the second conditional action sequence includes a proposed implementation of a second action of the particular set. 4. The system as recited in claim 1, wherein the one or more motion-causing directives are transmitted to the one or more motion control subsystems at a higher transmission rate than the first transmission rate. 5. The system as recited in claim 1, wherein to generate the plurality of conditional action sequences, the behavior planner is configured to utilize an analysis of recorded driver behavior. 6. The system as recited in claim 1, wherein to generate the plurality of conditional action sequences, the behavior planner is configured to utilize an analysis of simulated driver behavior. 7. The system as recited in claim 1, wherein prior to providing a particular conditional action sequence to the motion selector, the behavior planner is configured to: verify that the first conditional action sequence meets an acceptance criterion defined in terms of one or more of: (a) a timely arrival at a destination (b) physical feasibility (c) traffic rules compliance or (c) an estimated experience comfort level of a vehicle occupant. 8. The system as recited in claim 1, further comprising a lane-level planner; wherein the behavior planner is configured to generate the one or more conditional action sequences based at least in part on first set of data provided by the lane-level planner. 9. The system as recited in claim 8, further comprising a route planner, wherein the lane-level planner is configured to generate the first set of data based at least in part on another set of data generated by the route planner. 10. The system as recited in claim 1, wherein the behavior planner is configured to estimate, corresponding to a particular anticipated state corresponding to the first conditional action sequence, a value based at least in part on (a) an anticipated social interaction with an occupant of another vehicle, (b) an extent to which traffic rules are followed or (c) an anticipated comfort level of an occupant of the vehicle. 11. A method, comprising: performing, by one or more computing devices of a behavior planner for a vehicle: generating, using a combination of (a) a tree search algorithm and (b) heuristics produced by one or more machine learning models, a plurality of conditional action sequences with respect to the vehicle, including a first conditional action sequence and a second conditional action sequence, wherein the first conditional action sequence corresponds to a first set of anticipated states of the vehicle, and wherein the second conditional action sequence corresponds to a second set of anticipated states of the vehicle; and providing at least some conditional action sequences of the plurality of conditional action sequences to a motion selector of the vehicle; and generating, by the motion selector, based at least in part on (a) one or more conditional action sequences provided by the behavior planner and (b) respective data sets obtained from one or more sensors of the vehicle, one or more motion-control directives; and transmitting, by the motion selector, the one or more motion-control directives to one or more motion control subsystems of the vehicle. 12. The method as recited in claim 11, wherein the one or more machine learning models include (a) a first model trained using a supervised learning algorithm, and (b) a second model trained using a reinforcement learning algorithm. 13. The method as recited in claim 11, wherein at least one computing device of the one or more computing devices is located at a data center, the method further comprising: establishing a communication channel between the vehicle and the data center; wherein providing the at least some conditional action sequences comprises utilizing the communication channel. 14. The method as recited in claim 11, wherein generating the plurality of conditional action sequences comprises: utilizing a first computing device to generate the first conditional action sequence; utilizing a second computing device to generate the second conditional action sequence, wherein generation of at least a portion of the second conditional action sequence overlaps in time with generation of at least a portion of the first conditional action sequence. 15. The method as recited in claim 11, wherein the respective data sets comprise at least one data set corresponding to a partially-obstructed view. 16. A non-transitory computer-accessible storage medium storing program instructions that when executed on one or more processors implements a behavior planner for a vehicle, wherein the behavior planner is configured to: obtain respective data sets from one or more sensors of the vehicle; generate, using at least the respective data sets and a combination of (a) a tree search algorithm and (b) heuristics generated by one or more machine learning models, a first set of conditional action sequences with respect to the vehicle, including a first conditional action sequence and a second conditional action sequence, wherein the first conditional action sequence corresponds to a first set of anticipated states of the vehicle, and wherein the second conditional action sequence corresponds to a second set of anticipated states of the vehicle; provide one or more conditional action sequences of the first set as input to a motion selector of the vehicle, wherein the motion selector is configured to utilize the one or more conditional action sequences to issue one or more motion-control directives which cause the vehicle to move. 17. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the one or more machine learning models include (a) a first neural network model which provides a heuristic to optimize a tree search with respect to tree breadth, and (b) a second neural network model which provides a tree search to restrict a tree search with respect to tree depth. 18. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the behavior planner is configured to: utilize a statistical distribution of a particular set of actions taken by one or more entities under a first set of driving conditions, wherein the first conditional action sequence includes a proposed implementation of a first action of the particular set, and wherein the second conditional action sequence includes a proposed implementation of a second action of the particular set. 19. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the behavior planner is configured to generate the first set of conditional action sequences based at least in part on input received from one or more of: (a) a lane-level planner (b) a route planner. 20. The non-transitory computer-accessible storage medium as recited in claim 16, wherein the one or more sensors include one or more of: (a) an externally-oriented camera, (b) an occupant-oriented sensor, (c) a physiological signal detector, (d) a Global Positioning System (GPS) device, (e) a radar devices or (f) a LIDAR device.