Realistic and diverse simulation scenarios with reactive and feasible agent
behaviors can be used for validation and verification of self-driving system
performance without relying on expensive and time-consuming real-world testing.
Existing simulators rely on heuristic-based behavior models for background
vehicles, which cannot capture the complex interactive behaviors in real-world
scenarios. To bridge the gap between simulation and the real world, we propose
TrajGen, a two-stage trajectory generation framework, which can capture more
realistic behaviors directly from human demonstration. In particular, TrajGen
consists of the multi-modal trajectory prediction stage and the reinforcement
learning based trajectory modification stage. In the first stage, we propose a
novel auxiliary RouteLoss for the trajectory prediction model to generate
multi-modal diverse trajectories in the drivable area. In the second stage,
reinforcement learning is used to track the predicted trajectories while
avoiding collisions, which can improve the feasibility of generated
trajectories. In addition, we develop a data-driven simulator I-Sim that can be
used to train reinforcement learning models in parallel based on naturalistic
driving data. The vehicle model in I-Sim can guarantee that the generated
trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give
comprehensive metrics to evaluate generated trajectories for simulation
scenarios, which shows that TrajGen outperforms either trajectory prediction or
inverse reinforcement learning in terms of fidelity, reactivity, feasibility,
and diversity.

本论文提出了一个用于自动驾驶性能验证的仿真器 TrajGen，该仿真器基于自然驾驶数据进行强化学习，可以直接从人类演示中捕捉更真实的行为，并生成可靠而多样的行动轨迹来评估仿真场景中的性能表现。