Interpretable Decision-Making for End-to-End Autonomous Driving
This addresses the need for trustworthy AI in autonomous vehicles by improving interpretability, which correlates with safety, though it is incremental as it builds on existing end-to-end methods.
The paper tackles the problem of interpreting decisions in end-to-end autonomous driving by proposing loss functions that generate sparse and localized feature maps, resulting in a model that surpasses top-performing approaches on the CARLA benchmarks with lower infraction scores and the highest route completion rate.
Trustworthy AI is mandatory for the broad deployment of autonomous vehicles. Although end-to-end approaches derive control commands directly from raw data, interpreting these decisions remains challenging, especially in complex urban scenarios. This is mainly attributed to very deep neural networks with non-linear decision boundaries, making it challenging to grasp the logic behind AI-driven decisions. This paper presents a method to enhance interpretability while optimizing control commands in autonomous driving. To address this, we propose loss functions that promote the interpretability of our model by generating sparse and localized feature maps. The feature activations allow us to explain which image regions contribute to the predicted control command. We conduct comprehensive ablation studies on the feature extraction step and validate our method on the CARLA benchmarks. We also demonstrate that our approach improves interpretability, which correlates with reducing infractions, yielding a safer, high-performance driving model. Notably, our monocular, non-ensemble model surpasses the top-performing approaches from the CARLA Leaderboard by achieving lower infraction scores and the highest route completion rate, all while ensuring interpretability.