A Memristive Associative Learning Circuit for Fault-Tolerant Multi-Sensor Fusion in Autonomous Vehicles

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198464" target="_blank" >RIV/00216305:26220/26:0198464 - isvavai.cz</a>

Result on the web

<a href="https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/aisy.202500215" target="_blank" >https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/aisy.202500215</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/aisy.202500215" target="_blank" >10.1002/aisy.202500215</a>

Result language

angličtina

Original language name

A Memristive Associative Learning Circuit for Fault-Tolerant Multi-Sensor Fusion in Autonomous Vehicles

Original language description

Autonomous vehicles completely rely on accurate multi-sensor fusion to perceive their environment and make driving decisions. However, conventional AI-based perception systems face challenges in irregular conditions such as poor visibility, occlusions, or adverse weather conditions, which can lead to incomplete or degraded information from sensors reaching the central computing/navigation system. This severely impacts perception accuracy, potentially compromising vehicle, and pedestrian safety. This work presents a memristor-based associative learning circuit that enhances fault tolerance by dynamically adapting to multi-sensor inputs, including camera, LiDAR, radar, and ultrasonic sensors. The proposed circuit dynamically reinforces patterns, allowing the system to retain decision-making capabilities even when certain sensors fail or provide incomplete data. The fault tolerance of the circuit is validated through error analysis, proving that accurate outputs are generated even with missing sensor inputs. The system demonstrates an average error of 6.98% across 10 critical driving scenarios, with a power consumption of approximate to 152 mW per scenario, confirming its robustness, energy efficiency and adaptability in case of sensor failures and under-performance. The response time of the circuit has been optimized from milliseconds to seconds, aligning with realistic human-like reaction times required for autonomous navigation.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Advanced Intelligent Systems

ISSN

—

e-ISSN

2640-4567

Volume of the periodical

8

Issue of the periodical within the volume

1 (January 2026)

Country of publishing house

DE - GERMANY

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001525354100001

EID of the result in the Scopus database

2-s2.0-105009966535