Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles
This work addresses safety issues in self-driving vehicles by enhancing behavioral cloning with environmental feedback, though it is incremental as it builds on existing imitation learning methods.
The paper tackled the problem of insufficient safety in behavioral cloning for self-driving vehicles by introducing Social and Road loss functions that penalize predictions near non-drivable areas, resulting in improved safety metrics on a large-scale urban driving dataset.
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.