Learning Risk Level Set Parameters from Data Sets for Safer Driving

This paper examines how vehicles can quickly quantify the level of congestion in their environment for planning. We use risk level sets to define a metric of congestion for the vehicles. Using this metric, we can quickly identify distributions of environment and driver features, such as velocities and number of neighbors, based on risk within human driving data sets. We use the NGSIM and highD data sets to study how risk influences behaviors in city and highway driving. From these data sets, we learn common risk thresholds for classifying low, medium, and high-risk situations. Using these thresholds, we develop simulations of an autonomous vehicle driving along a highway, and demonstrate how the chosen risk threshold influences the autonomous vehicle behavior.

[1]  Mykel J. Kochenderfer,et al.  Multi-Agent Imitation Learning for Driving Simulation , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  I. Chakravarti,et al.  Handbook of Methods of Applied Statistics:@@@Volume I: Techniques of Computation, Descriptive Methods, and Statistical Inference@@@Volume II: Planning of Surveys and Experiments. , 1968 .

[3]  Bo Cheng,et al.  Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities , 2017 .

[4]  Mykel J. Kochenderfer,et al.  Generalizable intention prediction of human drivers at intersections , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[5]  Lutz Eckstein,et al.  Data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly Automated Vehicles , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[6]  Jianqiang Wang,et al.  Driving risk assessment using near-crash database through data mining of tree-based model. , 2015, Accident; analysis and prevention.

[7]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  Francesco Borrelli,et al.  Scenario model predictive control for lane change assistance on highways , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[9]  Wilko Schwarting,et al.  Recursive conflict resolution for cooperative motion planning in dynamic highway traffic , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[10]  D. Rus,et al.  Annual Review of Control , Robotics , and Autonomous Systems Planning and Decision-Making for Autonomous Vehicles , 2018 .

[11]  Daniela Rus,et al.  Navigating Congested Environments with Risk Level Sets , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Feng Guo,et al.  Individual driver risk assessment using naturalistic driving data. , 2013, Accident; analysis and prevention.

[13]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[14]  John M. Dolan,et al.  Learning Vehicle Surrounding-aware Lane-changing Behavior from Observed Trajectories , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[15]  Moshe Ben-Akiva,et al.  MODELS OF FREEWAY LANE CHANGING AND GAP ACCEPTANCE BEHAVIOR , 1996 .

[16]  Benjamin Coifman,et al.  A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset , 2017 .

[17]  Marcello Montanino,et al.  Making NGSIM Data Usable for Studies on Traffic Flow Theory , 2013 .

[18]  Mingyu Wang,et al.  Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Lutz Eckstein,et al.  The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[20]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data , 2008, 0804.0108.

[21]  D. Brillinger,et al.  Handbook of methods of applied statistics , 1967 .

[22]  Romain Billot,et al.  Microscopic cooperative traffic flow: calibration and simulation based on a next generation simulation dataset , 2014 .