Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

6 May 2022

Papers citing "Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation"

7 / 57 papers shown

Title
Critic Sequential Monte Carlo Vasileios Lioutas J. Lavington Justice Sefas Matthew Niedoba Yunpeng Liu Berend Zwartsenberg Setareh Dabiri Frank D. Wood Adam Scibior 40 7 0 30 May 2022
A Driver-Vehicle Model for ADS Scenario-based Testing Rodrigo Queiroz Divit Sharma Ricardo Diniz Caldas Krzysztof Czarnecki S. García Thorsten Berger Patrizio Pelliccione 6 10 0 05 May 2022
Fail-Safe Adversarial Generative Imitation Learning Philipp Geiger C. Straehle GAN 16 2 0 03 Mar 2022
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset Scott Ettinger Shuyang Cheng Benjamin Caine Chenxi Liu Hang Zhao ... Jiquan Ngiam Vijay Vasudevan Alexander McCauley Jonathon Shlens Drago Anguelov 137 531 0 20 Apr 2021
IntentNet: Learning to Predict Intention from Raw Sensor Data Sergio Casas Wenjie Luo R. Urtasun 3DPC 157 365 0 20 Jan 2021
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors Simon Suo S. Regalado Sergio Casas R. Urtasun 145 224 0 17 Jan 2021
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving Ming Zhou Jun-Jie Luo Julian Villela Yaodong Yang David Rusu ... H. Ammar Hongbo Zhang Wulong Liu Jianye Hao Jun Wang 134 193 0 19 Oct 2020