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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Conference on Modern Circuits and Systems Technologies

ISBN

979-8-3315-3915-3

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Dresden, Germany

Datum konání akce

11. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545636700047