Interpretable Decision-Making for End-to-End Autonomous Driving

TL;DR

This paper introduces a novel method for making end-to-end autonomous driving models more interpretable by generating sparse feature maps, which helps explain control decisions and improves safety and performance.

Contribution

The paper proposes loss functions that promote interpretability in deep neural networks for autonomous driving, resulting in better explainability and higher driving performance.

Findings

Model surpasses top CARLA leaderboard approaches in infractions and route completion.

Interpretability correlates with reduced infractions and safer driving.

Method enhances trustworthiness of autonomous driving AI.

Abstract

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.