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Science·P Space·Evidence-backed problem·Published 2026-09-04

Explainable deep learning improves human mental models of self-driving cars

Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 . Although research into interpreting these systems has surged, most of it is confined to simulations or toy setups because of the difficulty of real-world deployment 10,11 , leaving the practical utility of these techniques unknown. Here, we introduce the Conce…

TRV-2026-0981Peer-reviewedPermanent record — cite & verify
Explainable deep learning improves human mental models of self-driving cars

11Can-Deep-Sea-Water-Be-Processed-into-Potable-Water-and-Distributed-into-the-Middle-East by Federal Ministry Water. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .

Although research into interpreting these systems has surged, most of it is confined to simulations or toy setups because of the difficulty of real-world deployment 10,11 , leaving the practical utility of these techniques unknown. Here, we introduce the Concept-Wrapper Network (CW-Net), a method for faithfully explaining the behaviour of machine-learning-based planners that causally grounds their reasoning in human-interpretable concepts without sacrificing performance.

Main points
  • Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 .
  • The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .
  • Although research into interpreting these systems has surged, most of it is confined to simulations or toy setups because of the difficulty of real-world deployment 10,11 , leaving the practical utility of these techniques unknown.
Problem

Explainable deep learning improves human mental models of self-driving cars: The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .

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