Preventing False Activations in Autonomous Vehicles: A Memristive Associative Learning Approach with Selective Sensor Pairing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199633" target="_blank" >RIV/00216305:26220/26:0199633 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/11083932" target="_blank" >https://ieeexplore.ieee.org/document/11083932</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MOCAST65744.2025.11083932" target="_blank" >10.1109/MOCAST65744.2025.11083932</a>

Result language

angličtina

Original language name

Preventing False Activations in Autonomous Vehicles: A Memristive Associative Learning Approach with Selective Sensor Pairing

Original language description

Autonomous vehicles rely on multi-sensor fusion for accurate perception and decision-making. However, conventional sensor-based learning circuits struggle to differentiate between incomplete and valid sensor inputs, leading to erroneous activations. This paper presents a memristive associative learning circuit with selective sensor pairing and temporal validation to enhance fault tolerance in autonomous driving. Unlike existing associative learning circuits that generate outputs in untrained states with partial sensor data, the proposed design enforces a strict, electronically adjustable temporal window between sensor inputs before triggering an output. This mechanism prevents false activations from isolated or delayed signals, ensuring decisions are based on complete situational awareness. The circuit implemented using a memristor, operational amplifiers (OP-AMPs), logic gates, and latches, achieves power consumption below 300 mW, making it a low-power and efficient solution. Simulation results show that the proposed circuit reduces erroneous activations by 60%, achieving an average error rate of 0.96%, compared to 30.98% in a traditional associative learning circuit during pedestrian detection scenarios. Our finding demonstrates that selective sensor pairing, and temporal validation significantly improve the reliability and safety of autonomous vehicle navigation, particularly in challenging environments where sensor signals may be delayed or partially available.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

Article name in the collection

International Conference on Modern Circuits and Systems Technologies

ISBN

979-8-3315-3915-3

ISSN

—

e-ISSN

—

Number of pages

4

Pages from-to

1-4

Publisher name

IEEE

Place of publication

NEW YORK

Event location

Dresden, Germany

Event date

Jun 11, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001545636700047