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Found 8 papers, 3 papers with code
RAP: Risk-Aware Prediction for Robust Planning
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions.
Control-Aware Prediction Objectives for Autonomous Driving
Autonomous vehicle software is typically structured as a modular pipeline of individual components (e. g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks.
Dynamics-Aware Comparison of Learned Reward Functions
The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world.
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation.
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify.
Multi-Agent Imitation Learning for Driving Simulation
Simulation is an appealing option for validating the safety of autonomous vehicles.
Real-time Prediction of Intermediate-Horizon Automotive Collision Risk
Advanced collision avoidance and driver hand-off systems can benefit from the ability to accurately predict, in real time, the probability a vehicle will be involved in a collision within an intermediate horizon of 10 to 20 seconds.
