11 citations · 16 across the 7 of their papers we have counts for
5 papers · 1 filter
Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction
Zhangjie Cao, Erdem Bıyık, Guy Rosman +1
Multi-agent interactions are important to model for forecasting other agents' behaviors and trajectories. At a certain time, to forecast a reasonable future trajectory, each agent…
Risk Conditioned Neural Motion Planning
Xin Huang, Meng Feng, Ashkan Jasour +2
Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. While existing mathematical programming methods offer theoretical guarantees in the co…
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama +7
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the…
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama +6
Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic pa…
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
Zhangjie Cao, Erdem Bıyık, Woodrow Z. Wang +4
Autonomous driving has achieved significant progress in recent years, but autonomous cars are still unable to tackle high-risk situations where a potential accident is likely. In s…