Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

Understand

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices.

• However, a lack of explainability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles.
• Moreover, the issue is exacerbated when these vehicles are involved in or cause traffic accidents.
• Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation.