Mitigating cascading effects of vehicle lane changes: A hyperedge game approach DOI
Yunfei Li, Dongyu Luo, Jiangfeng Wang

et al.

Transportation Research Part C Emerging Technologies, Journal Year: 2024, Volume and Issue: 171, P. 104971 - 104971

Published: Dec. 28, 2024

Language: Английский

A two-stage framework for parking search behavior prediction through adversarial inverse reinforcement learning and transformer DOI
Ji Tianyi, Cong Zhao, Yuxiong Ji

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124548 - 124548

Published: June 25, 2024

Language: Английский

Citations

4
Comfortable driving control for connected automated vehicles based on deep reinforcement learning and knowledge transfer DOI Creative Commons

Chuna Wu,

Jing Chen,

Jinqiang Yao

et al.

IET Intelligent Transport Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 8, 2024

Abstract With the development of connected automated vehicles (CAVs), preview and large‐scale road profile information detected by different become available for speed planning active suspension control CAVs to enhance ride comfort. Existing methods are not well adapted rough pavements districts, where distributions roughness significantly because traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework coordinating with knowledge transfer. Based on existing approaches, deep reinforcement learning (DRL) algorithm is designed learn strategies information. Fine‐tuning lateral connection adopted transfer learned adaptability in districts. DRL‐based models trained transferred using real‐world pavement data districts Shanghai, China. The experimental results show that proposed method increases vertical comfort 41.10% pavements, compared model predictive control. proven be applicable stochastic CAVs.

Language: Английский

Citations

1
Learning to search for parking like a human: A deep inverse reinforcement learning approach DOI Creative Commons
Shiyu Wang, H. B. Yang, Yuzhong Tang

et al.

International Journal of Transportation Science and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

1
An Adaptive Vehicle Detection Model for Traffic Surveillance of Highway Tunnels Considering Luminance Intensity DOI Creative Commons

Yongke Wei,

Zimu Zeng,

Tingquan He

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(18), P. 5912 - 5912

Published: Sept. 12, 2024

Vehicle detection is essential for road traffic surveillance and active safety management. Deep learning methods have recently shown robust feature extraction capabilities achieved improved results. However, vehicle models often perform poorly under abnormal lighting conditions, especially in highway tunnels. We proposed an adaptive model that accounts varying luminance intensities to address this issue. The categorizes the image data into normal scenarios. employ CycleGAN with edge loss as adjustment module This adjusts brightness of images a level through generative network. Finally, YOLOv7 utilized detection. experimental results demonstrate our effectively detects vehicles scenarios can mitigate generation distortion. Under scenarios, 16.3% improvement precision, 1.7% recall, 9.8% mAP_0.5 compared original YOLOv7. Additionally, transferable enhance accuracy other models.

Language: Английский

Citations

0
Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention DOI
Xinrong Zhang, Jiaxuan Cai,

Fuzhou Chen

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 188, P. 115604 - 115604

Published: Oct. 3, 2024

Language: Английский

Citations

0
Mitigating cascading effects of vehicle lane changes: A hyperedge game approach DOI
Yunfei Li, Dongyu Luo, Jiangfeng Wang

et al.

Transportation Research Part C Emerging Technologies, Journal Year: 2024, Volume and Issue: 171, P. 104971 - 104971

Published: Dec. 28, 2024

Language: Английский

Citations

0