Human Driver Behavior Prediction based on UrbanFlow*

How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers’ behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in urban intersection scenarios. Meanwhile, in order to collect human drivers’ driving behavior data in the urban scenario, we describe a system called UrbanFlow which includes the whole procedure from raw bird’s-eye view data collection via drone to the final processed trajectories. The system is mainly intended for urban scenarios but can be extended to be used for any traffic scenarios.

[1]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[2]  Wolfram Burgard,et al.  Socially compliant mobile robot navigation via inverse reinforcement learning , 2016, Int. J. Robotics Res..

[3]  Rakesh Gupta,et al.  Turn prediction at generalized intersections , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[4]  Amaury Nègre,et al.  Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety , 2011, IEEE Intelligent Transportation Systems Magazine.

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

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

[7]  Trevor Darrell,et al.  BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling , 2018, ArXiv.

[8]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[9]  Rüdiger Dillmann,et al.  Learning context sensitive behavior models from observations for predicting traffic situations , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[10]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[12]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[13]  John M. Dolan,et al.  Interactive Trajectory Prediction for Autonomous Driving via Recurrent Meta Induction Neural Network , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[14]  Christoph Stiller,et al.  Driver intent inference at urban intersections using the intelligent driver model , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[15]  Jonathan P. How,et al.  Threat assessment design for driver assistance system at intersections , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[16]  Georgios D. Evangelidis,et al.  Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[18]  Y. Chan,et al.  A Kalman Filter Based Tracking Scheme with Input Estimation , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Vittorio Ferrari,et al.  COCO-Stuff: Thing and Stuff Classes in Context , 2016, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Alexey Shvets,et al.  TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation , 2018, Computer-Aided Analysis of Gastrointestinal Videos.

[21]  John M. Dolan,et al.  POMDP and Hierarchical Options MDP with Continuous Actions for Autonomous Driving at Intersections , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[22]  John M. Dolan,et al.  Automatically Generated Curriculum based Reinforcement Learning for Autonomous Vehicles in Urban Environment , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[23]  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).