论文标题
增强行为克隆，自动驾驶汽车的环境损失
Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles
论文作者
论文摘要
学识渊博的路径计划者由于能够对人类驾驶行为进行建模和快速推断的能力而引起了研究的兴趣。关于行为克隆的最新作品表明，对专家观察的简单模仿不足以处理复杂的驾驶场景。此外，预测可驾驶区域以外的地方可能导致潜在的危险情况。本文提出了一系列损失职能，即社会损失和道路损失，这解释了道路计划中有风险的社交互动。这些损失充当了围绕不可驱动区域的排斥标量场。预测在这些地区附近的降落以更高的培训成本产生的预测，这可以最大程度地减少返回范围。该方法为传统的监督学习设置提供了其他环境反馈。我们在大规模的城市驾驶数据集上验证了这种方法。结果表明，代理商学会了模仿人类驾驶，同时展示更好的安全指标。此外，所提出的方法对推论具有积极影响，而无需人为地产生不安全的驾驶示例。这项解释性研究表明，与经典的行为克隆相比，所获得的好处与代理商决策中不可驱动区域的相关性更高。
Learned path planners have attracted research interest due to their ability to model human driving behavior and rapid inference. Recent works on behavioral cloning show that simple imitation of expert observations is not sufficient to handle complex driving scenarios. Besides, predictions that land outside drivable areas can lead to potentially dangerous situations. This paper proposes a set of loss functions, namely Social loss and Road loss, which account for modelling risky social interactions in path planning. These losses act as a repulsive scalar field that surrounds non-drivable areas. Predictions that land near these regions incur in a higher training cost, which is minimized using backpropagation. This methodology provides additional environment feedback to the traditional supervised learning set up. We validated this approach on a large-scale urban driving dataset. The results show the agent learns to imitate human driving while exhibiting better safety metrics. Furthermore, the proposed methodology has positive effects on inference without the need to artificially generate unsafe driving examples. The explanability study suggests that the benefits obtained are associated with a higher relevance of non-drivable areas in the agent's decisions compared to classical behavioral cloning.
