Humans learn to drive through both practice and theory, for example, by studying the rules,
while most self‐driving systems are limited to the former. Being able to incorporate human …

Humans learn to drive through both practice and theory, eg by studying the rules, while most
self-driving systems are limited to the former. Being able to incorporate human knowledge of …

Deep neural perception and control networks have become key components of self-driving
vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations …

Deep neural perception and control networks are likely to be a key component of self-driving
vehicles. These models need to be explainable-they should provide easy-to-interpret …

Explainable AI, in the context of autonomous systems, like self-driving cars, has drawn broad
interests from researchers. Recent studies have found that providing explanations for …

Recent success suggests that deep neural control networks are likely to be a key component
of self-driving vehicles. These networks are trained on large datasets to imitate human …

Autonomous driving is advancing at a fast pace, with driving algorithms becoming more and
more accurate and reliable. Despite this, it is of utter importance to develop models that can …

This work explores scene graphs as a distilled representation of high-level information for
autonomous driving, applied to future driver-action prediction. Given the scarcity and strong …

Current deep learning based autonomous driving approaches yield impressive results also
leading to in-production deployment in certain controlled scenarios. One of the most popular …

In this era of active development of autonomous vehicles, it becomes crucial to provide
driving systems with the capacity to explain their decisions. In this work, we focus on …