GAMMA: A General Agent Motion Prediction Model for Autonomous Driving

Autonomous driving in mixed traffic requires reliable motion prediction of nearby traffic agents such as pedestrians, bicycles, cars, buses, etc.. This prediction problem is extremely challenging because of the diverse dynamics and geometry of traffic agents, complex road conditions, and intensive interactions among the agents. In this paper, we proposed GAMMA, a general agent motion prediction model for autonomous driving, that can predict the motion of heterogeneous traffic agents with different kinematics, geometry, human agents' inner states, etc.. GAMMA formalizes motion prediction as geometric optimization in the velocity space, and integrates physical constraints and human inner states into this unified framework. Our results show that GAMMA outperforms state-of-the-art approaches significantly on diverse real-world datasets.

[1]  Philip H. S. Torr,et al.  DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Mohan M. Trivedi,et al.  Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[3]  Rainald Löhner,et al.  On the modeling of pedestrian motion , 2010 .

[4]  Dinesh Manocha,et al.  Real-time reciprocal collision avoidance with elliptical agents , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Julien Pettré,et al.  Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories With GANs , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Luis E. Ortiz,et al.  Who are you with and where are you going? , 2011, CVPR 2011.

[7]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[8]  Volker Willert,et al.  Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Dinesh Manocha,et al.  PORCA: Modeling and Planning for Autonomous Driving Among Many Pedestrians , 2018, IEEE Robotics and Automation Letters.

[10]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Silvio Savarese,et al.  Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Dinesh Manocha,et al.  Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT , 2018, AAMAS.

[13]  Yun-Pang Flötteröd,et al.  Microscopic Traffic Simulation using SUMO , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[14]  Silvio Savarese,et al.  Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Paul A. Beardsley,et al.  Reciprocal collision avoidance for multiple car-like robots , 2012, 2012 IEEE International Conference on Robotics and Automation.

[16]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[17]  Ying Nian Wu,et al.  Multi-Agent Tensor Fusion for Contextual Trajectory Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Dinesh Manocha,et al.  Generalized velocity obstacles , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Daniel Thalmann,et al.  Torso Crowds , 2017, IEEE Transactions on Visualization and Computer Graphics.

[20]  Florent Altché,et al.  An LSTM network for highway trajectory prediction , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[21]  Daniela Rus,et al.  Context and Intention Aware Planning for Urban Driving , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Paul A. Beardsley,et al.  Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots , 2010, DARS.

[23]  Mohan M. Trivedi,et al.  How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction , 2018, IEEE Transactions on Intelligent Vehicles.

[24]  Véronique Berge-Cherfaoui,et al.  Vehicle trajectory prediction based on motion model and maneuver recognition , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Chung Choo Chung,et al.  Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[26]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[27]  S. Savarese,et al.  Learning an Image-Based Motion Context for Multiple People Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Dinesh Manocha,et al.  Reciprocal collision avoidance with acceleration-velocity obstacles , 2011, 2011 IEEE International Conference on Robotics and Automation.

[29]  Dinesh Manocha,et al.  Efficient and Safe Vehicle Navigation Based on Driver Behavior Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[30]  Mohan M. Trivedi,et al.  Convolutional Social Pooling for Vehicle Trajectory Prediction , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

[32]  Silvio Savarese,et al.  SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Nanning Zheng,et al.  SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[35]  Luc Van Gool,et al.  Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings , 2010, ECCV.

[36]  Dinesh Manocha,et al.  TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents , 2018, AAAI.

[37]  Dinesh Manocha,et al.  The Hybrid Reciprocal Velocity Obstacle , 2011, IEEE Transactions on Robotics.

[38]  Gonzalo Ferrer,et al.  Robot companion: A social-force based approach with human awareness-navigation in crowded environments , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[39]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[40]  Dinesh Manocha,et al.  TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).