基于分层策略抽象的示范学习：面向高性能且礼貌的自动驾驶赛车
Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing
AI总结：
提出一种基于分层策略抽象的自动驾驶赛车框架，从专家示范学习轨迹规划与残差控制，在高保真模拟中显著提升性能并实现礼貌超车。
摘要：
完全自动驾驶赛车不仅要求高速驾驶，还要求公平且礼貌的 maneuvers。在本文中，我们提出了一种自动驾驶赛车框架，该框架利用分层策略抽象从专家示范中学习复杂的赛车行为。在轨迹层面，我们的策略模型预测一个密集分布图，指示从离线示范中学习到的轨迹的可能性。最大似然轨迹随后被传递给控制层策略，该策略以残差方式生成控制输入，考虑极限性能下的车辆动力学。我们在高保真赛车模拟器中评估了我们的框架，并在具有挑战性的多智能体对抗场景中与竞争基线进行了比较。定量和定性结果表明，我们的轨迹规划策略显著优于基线，残差控制策略改善了圈速和跟踪精度。此外，与十个对手进行的具有挑战性的闭环实验表明，我们的框架能够通过理解细微的交互来超越其他车辆，像专业车手一样有效平衡性能与礼貌。
英文摘要：
Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex racing behaviors from expert demonstrations using hierarchical policy abstractions. At the trajectory level, our policy model predicts a dense distribution map indicating the likelihood of trajectories learned from offline demonstrations. The maximum likelihood trajectory is then passed to the control-level policy, which generates control inputs in a residual fashion, considering vehicle dynamics at the limits of performance. We evaluate our framework in a high-fidelity racing simulator and compare it against competing baselines in challenging multi-agent adversarial scenarios. Quantitative and qualitative results show that our trajectory planning policy significantly outperforms the baselines, and the residual control policy improves lap time and tracking accuracy. Moreover, challenging closed-loop experiments with ten opponents show that our framework can overtake other vehicles by understanding nuanced interactions, effectively balancing performance and courtesy like professional drivers.
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。
