Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles

2 February 2024

Papers citing "Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles"

11 / 11 papers shown

Title
Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning Hongyu Guo Kun Xie M. Keyvan-Ekbatani 58 37 0 29 Sep 2022
A Formal Safety Characterization of Advanced Driver Assist Systems in the Car-Following Regime with Scenario-Sampling Bowen Weng Minghao Zhu Keith A. Redmill 29 6 0 17 Feb 2022
A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety (extended version) L. Capito Bowen Weng Ü. Özgüner Keith A. Redmill 30 11 0 25 Sep 2020
SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction Sriram Narayanan Buyu Liu Francesco Pittaluga Manmohan Chandraker 44 26 0 26 Jul 2020
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies Sampo Kuutti Saber Fallah Richard Bowden AAML 42 49 0 27 Feb 2020
Social Attention for Autonomous Decision-Making in Dense Traffic Edouard Leurent Jean Pierre Mercat 49 85 0 27 Nov 2019
Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques Ding Zhao Henry Lam H. Peng S. Bao D. Leblanc Kazutoshi Nobukawa C. S. Pan 55 355 0 16 May 2016
Dueling Network Architectures for Deep Reinforcement Learning Ziyun Wang Tom Schaul Matteo Hessel H. V. Hasselt Marc Lanctot Nando de Freitas OffRL 91 3,749 0 20 Nov 2015
Prioritized Experience Replay Tom Schaul John Quan Ioannis Antonoglou David Silver OffRL 210 3,787 0 18 Nov 2015
Deep Reinforcement Learning with Double Q-learning H. V. Hasselt A. Guez David Silver OffRL 156 7,623 0 22 Sep 2015
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 114 12,201 0 19 Dec 2013