Explaining Autonomous Driving by Learning End-to-End Visual Attention

by   Luca Cultrera, et al.

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 and fascinating approaches relies on learning vehicle controls directly from data perceived by sensors. This end-to-end learning paradigm can be applied both in classical supervised settings and using reinforcement learning. Nonetheless the main drawback of this approach as also in other learning problems is the lack of explainability. Indeed, a deep network will act as a black-box outputting predictions depending on previously seen driving patterns without giving any feedback on why such decisions were taken. While to obtain optimal performance it is not critical to obtain explainable outputs from a learned agent, especially in such a safety critical field, it is of paramount importance to understand how the network behaves. This is particularly relevant to interpret failures of such systems. In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.


page 4

page 8


What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving

More research attention has recently been given to end-to-end autonomous...

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

Self-driving cars and autonomous driving research has been receiving con...

Development and testing of an image transformer for explainable autonomous driving systems

In the last decade, deep learning (DL) approaches have been used success...

Reason induced visual attention for explainable autonomous driving

Deep learning (DL) based computer vision (CV) models are generally consi...

Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

Learning to drive faithfully in highly stochastic urban settings remains...

Explicit Domain Adaptation with Loosely Coupled Samples

Transfer learning is an important field of machine learning in general, ...

Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving

End-to-end autonomous driving has made impressive progress in recent yea...

Please sign up or login with your details

Forgot password? Click here to reset