Detection of Emergency Braking Intention Based on Pedal-Switching Process

The driver’s reaction time and the brake actuator response time during emergency braking play a crucial role in braking distance. Detecting the driver’s braking intention and reducing actuator reaction time can enhance the safety advantages of the intelligent braking system. This study introduces a braking intent recognition method based on detecting the driver’s transition between the accelerator and brake pedals. This approach can seamlessly integrate with the brake-by-wire (BBW) system, leveraging the advantages of complete decoupling between the brake pedal and the actuator. This study initially examines the braking process and then presents the proposed braking intent recognition technique. The driver’s pedal-switching action is characterized by a time-varying vector, which is integrated into the detection module. Various feature extraction methods are introduced to analyze the accelerator release speed and time-varying vectors under emergency and normal braking scenarios separately. Leveraging these features, a predictive model is developed using machine learning methods. The effectiveness of the proposed detection system and predictive model is validated through pseudo-online simulations. Results demonstrate a high recognition accuracy of 96.2% for the emergency braking intention detection system. Moreover, the proposed method shows the capability to predict braking intentions about 36 ms prior to the brake pedal being pressed during emergency braking and about 589 ms in advance during normal braking. The proposed approach has the potential to reduce braking distances significantly, serving as a valuable enhancement to Active Driving Assistance Systems (ADAS) for improved braking safety.

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Publication Details

Journal
IEEE Access
Published
2024-11-27
DOI
https://doi.org/10.1109/access.2024.3506599
Citations
1
Primary Topic
Mobile Agent-Based Network Management
Type
article
Field-Weighted Citation Impact
0.35
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article

Detection of Emergency Braking Intention Based on Pedal-Switching Process

Zhichao Feng, Chengleng Han, C. F. Xu, Shicheng Li et al.
1 citations
IEEE Access
Mobile Agent-Based Network Management
0.35
article

Detection of Emergency Braking Intention Based on Pedal-Switching Process

Zhichao Feng, Chengleng Han, C. F. Xu, Shicheng Li, Lin Xu
article en
1 citations

Abstract

The driver’s reaction time and the brake actuator response time during emergency braking play a crucial role in braking distance. Detecting the driver’s braking intention and reducing actuator reaction time can enhance the safety advantages of the intelligent braking system. This study introduces a braking intent recognition method based on detecting the driver’s transition between the accelerator and brake pedals. This approach can seamlessly integrate with the brake-by-wire (BBW) system, leveraging the advantages of complete decoupling between the brake pedal and the actuator. This study initially examines the braking process and then presents the proposed braking intent recognition technique. The driver’s pedal-switching action is characterized by a time-varying vector, which is integrated into the detection module. Various feature extraction methods are introduced to analyze the accelerator release speed and time-varying vectors under emergency and normal braking scenarios separately. Leveraging these features, a predictive model is developed using machine learning methods. The effectiveness of the proposed detection system and predictive model is validated through pseudo-online simulations. Results demonstrate a high recognition accuracy of 96.2% for the emergency braking intention detection system. Moreover, the proposed method shows the capability to predict braking intentions about 36 ms prior to the brake pedal being pressed during emergency braking and about 589 ms in advance during normal braking. The proposed approach has the potential to reduce braking distances significantly, serving as a valuable enhancement to Active Driving Assistance Systems (ADAS) for improved braking safety.

IEEE AccessVol. 13
Wuhan University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 36%
Mobile Agent-Based Network Management
0.35
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