基于可微交通仿真的交通感知自动驾驶
Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation
AI总结：
提出交通感知自动驾驶方法TrAAD，通过引入可微交通仿真信息指导模仿学习的速度控制监督，使自动驾驶车辆学会优化交通流并降低整体能耗。
摘要：
尽管自动驾驶控制和交通仿真领域已取得进展，但鲜有研究探索将二者与深度学习相结合。这两个领域的研究似乎专注于完全不同的独立问题，然而在现实世界中，交通与驾驶本质上是相关的。本文提出了交通感知自动驾驶（TrAAD），这是一种用于交通信息模仿学习的可泛化蒸馏式方法，直接优化以实现更快的交通流和更低的能耗。TrAAD专注于模仿学习系统中的速度控制监督，因为大多数驾驶研究侧重于感知和转向。此外，我们的方法解决了交通与驾驶仿真器之间缺乏协同仿真的问题，并为未来工作中将交通仿真直接融入自动驾驶提供了基础。结果表明，通过在模仿学习方法的监督中引入交通仿真信息，自动驾驶车辆能够学会以有利于交通流和所有附近车辆整体能耗的方式进行加速。
英文摘要：
While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring their unification with deep learning. Works in both areas seem to focus on entirely different exclusive problems, yet traffic and driving are inherently related in the real world. In this paper, we present Traffic-Aware Autonomous Driving (TrAAD), a generalizable distillation-style method for traffic-informed imitation learning that directly optimizes for faster traffic flow and lower energy consumption. TrAAD focuses on the supervision of speed control in imitation learning systems, as most driving research focuses on perception and steering. Moreover, our method addresses the lack of co-simulation between traffic and driving simulators and provides a basis for directly involving traffic simulation with autonomous driving in future work. Our results show that, with information from traffic simulation involved in the supervision of imitation learning methods, an autonomous vehicle can learn how to accelerate in a fashion that is beneficial for traffic flow and overall energy consumption for all nearby vehicles.
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。
