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Paper FR-LM-T44.3

Chen, Yitao (Toyota Motor North America), Han, Kyungtae (Toyota Motor North America), Moradipari, Ahmadreza (Toyota InfoTec Lab), Avedisov, Sergei (Toyota Motor North America R&D - InfoTech Labs), Mishra, Shatadal (Toyota Infotech R&D Labs), Gupta, Rohit (Toyota Motor North America R&D), Altintas, Onur (Toyota North America R&D)

Real-Time Gap and Acceleration Adaptation in Adaptive Cruise Control Based on Driver Overrides

Scheduled for presentation during the Regular Session "S44a-Human Factors and Human Machine Interaction in Automated Driving" (FR-LM-T44), Friday, November 21, 2025, 11:10−11:30, Currumbin

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Driver Behavior Monitoring and Feedback Systems for Semi-autonomous Vehicles, Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety

Abstract

Advanced Driver Assistance Systems (ADAS) have significantly contributed to driving safety and comfort. Systems, such as Adaptive Cruise Control (ACC), are deployed in modern vehicles. However, traditional ADAS are typically designed with a one-size-fits-all approach, neglecting the diversity in individual driving preferences.

To address this, this paper presents a real-time Personalized Adaptive Cruise Control (P-ACC) system that dynamically adjusts car-following behavior by adapting both gap and acceleration settings based on individual driver overrides. Unlike existing personalized ACC systems, which rely on historical data and periodic updates, the proposed P-ACC system leverages natural driver override actions to update the ACC speed-gap and speed-acceleration table instantaneously. We implement and evaluate the P-ACC system, which uniquely integrates real-time behavioral adaptation with an intuitive override interface, in a real vehicle on a controlled test track. Our results demonstrate a 78% and 53% reduction in manual gap and acceleration setting changes compared to standard ACC, highlighting the system's ability to align with driver preferences under realistic driving conditions. This work advances the feasibility of behavior-driven, real-time ADAS adaptation, enhancing driver satisfaction and safety through immediate and personalized driving assistance.

 

 

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