TrafficSim：学习模拟真实的多智能体行为
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
摘要：
仿真具有大规模评估自动驾驶系统的潜力，从而实现快速开发和安全部署。为了缩小仿真和现实世界之间的差距，我们需要模拟真实的多智能体行为。现有的仿真环境依赖于直接编码交通规则的基于几何学的模型，其不能捕获不规则的机动（例如，轻推，U形转弯）和复杂的相互作用（例如，屈服、合并）。相比之下，我们利用真实世界的数据直接从人类演示中学习，从而捕捉到更多样化的演员行为。为此，我们提出了一个多智能体的行为模型，现实的交通模拟的marticsim。特别是，我们利用隐式潜变量模型参数化的联合演员的政策，产生社会一致的计划，在现场的所有演员联合。为了学习适合长期模拟的鲁棒策略，我们在训练中展开策略，并通过随时间的完全可微模拟进行优化。我们的学习目标既包括人类示范，也包括常识。我们表明，与一组不同的基线相比，EschericSim生成的流量场景更加真实和多样化。值得注意的是，我们可以利用轨迹生成的MixicSim作为有效的数据增强训练更好的运动规划。
英文摘要：
Simulation has the potential to massively scale evaluation of self-driving systems enabling rapid development as well as safe deployment. To close the gap between simulation and the real world, we need to simulate realistic multi-agent behaviors. Existing simulation environments rely on heuristic-based models that directly encode traffic rules, which cannot capture irregular maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding, merging). In contrast, we leverage real-world data to learn directly from human demonstration and thus capture a more diverse set of actor behaviors. To this end, we propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy that generates socially-consistent plans for all actors in the scene jointly. To learn a robust policy amenable for long horizon simulation, we unroll the policy in training and optimize through the fully differentiable simulation across time. Our learning objective incorporates both human demonstrations as well as common sense. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines. Notably, we can exploit trajectories generated by TrafficSim as effective data augmentation for training better motion planner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
