Self-driving cars struggle to see at night or in fog – but imitating the human brain can make them safe

2026-05-12 · Source: Artificial intelligence (AI) – The Conversation · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Researchers at the University of Valencia have developed a novel AI vision system for autonomous vehicles that significantly improves performance in adverse environmental conditions like fog and darkness. Current AI systems excel in clear visibility but fail when conditions change, unlike human drivers. The new approach integrates a "divisive normalisation" mechanism, inspired by how human brain neurons adapt to varying light levels by amplifying weak signals and dampening strong ones. By adding layers to existing AI models to simulate this biological "volume control," the modified systems demonstrated over 20% better performance than unmodified counterparts in simulated driving tests under fog and darkness, maintaining the ability to distinguish objects like cars from buildings and roads. This research suggests that biologically inspired designs can enhance AI robustness without requiring more powerful hardware or larger datasets.

Key takeaway

For research scientists developing autonomous driving systems, you should explore biologically inspired mechanisms like divisive normalisation to enhance AI robustness. This approach offers a path to significantly improve perception in challenging real-world conditions such as fog and darkness, making self-driving cars safer and more reliable without needing extensive data or computational upgrades.

Key insights

Biologically inspired divisive normalisation significantly enhances AI vision system robustness in adverse conditions.

Principles

• AI robustness benefits from biological inspiration.
• Divisive normalisation adapts signal processing.
• Nature offers solutions to AI challenges.

Method

The method involves adding layers to existing AI models to simulate divisive normalisation, forcing neurons to communicate and adapt to environmental changes, thereby enhancing detail capture in challenging visual conditions.

In practice

• Integrate divisive normalisation into vision models.
• Test AI systems across diverse environmental conditions.
• Prioritize robustness over raw accuracy in ideal settings.

Topics

• Self-driving Cars
• AI Vision Systems
• Divisive Normalisation
• Biologically Inspired AI
• Low Visibility Driving

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence (AI) – The Conversation.