TY - GEN
T1 - A Adaptive Collision Warning System Based on the Recognition of Slippery Road Conditions
AU - Cai, Mingjiang
AU - Cheng, Ying
AU - Zhang, Rui
AU - Yang, Shijuan
AU - Zhao, Yanan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Aiming at the problems of slow detection speed, large prediction error of warning area and weak environmental adaptability of the current machine vision-based vehicle collision warning technology, this paper proposes a collision warning system based on the recognition of slippery road conditions. Firstly, this paper uses the on-board camera to monitor the environment and road conditions in front of the vehicle in real time, and uses the YOLOv5 algorithm to detect the vehicle in front of it in real time, while accurately identifying the current wet state of the road, such as dry and slippery, through the ResNet50 model in the convolutional neural network. Secondly, a driving safety distance model with adaptive traffic environment characteristics is established by combining different road environments and driving conditions, and an early warning area is generated that changes dynamically with the speed of the vehicle and the slippery state of the road. Finally, possible collisions are predicted and warned in time, based on the relationship between the area of the warning and the position of the vehicle. Experimental results show that the method proposed in this paper improves the overall warning accuracy by 6.72% and reduces the warning false alarm rate for oncoming traffic on both sides by 16.67% compared with the traditional risk warning algorithm. Its application in practical driving can effectively ensure the safety of the driver and has a high application value.
AB - Aiming at the problems of slow detection speed, large prediction error of warning area and weak environmental adaptability of the current machine vision-based vehicle collision warning technology, this paper proposes a collision warning system based on the recognition of slippery road conditions. Firstly, this paper uses the on-board camera to monitor the environment and road conditions in front of the vehicle in real time, and uses the YOLOv5 algorithm to detect the vehicle in front of it in real time, while accurately identifying the current wet state of the road, such as dry and slippery, through the ResNet50 model in the convolutional neural network. Secondly, a driving safety distance model with adaptive traffic environment characteristics is established by combining different road environments and driving conditions, and an early warning area is generated that changes dynamically with the speed of the vehicle and the slippery state of the road. Finally, possible collisions are predicted and warned in time, based on the relationship between the area of the warning and the position of the vehicle. Experimental results show that the method proposed in this paper improves the overall warning accuracy by 6.72% and reduces the warning false alarm rate for oncoming traffic on both sides by 16.67% compared with the traditional risk warning algorithm. Its application in practical driving can effectively ensure the safety of the driver and has a high application value.
KW - Collision warning
KW - Intelligent driving
KW - Slippery road condition recognition
KW - Target detection
UR - http://www.scopus.com/inward/record.url?scp=85206171156&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3052-0_30
DO - 10.1007/978-981-97-3052-0_30
M3 - Conference contribution
AN - SCOPUS:85206171156
SN - 9789819730513
T3 - Lecture Notes in Electrical Engineering
SP - 423
EP - 432
BT - Smart Transportation and Green Mobility Safety - Traffic Safety
A2 - Wang, Wuhong
A2 - Guo, Hongwei
A2 - Jiang, Xiaobei
A2 - Shi, Jian
A2 - Sun, Dongxian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Green Intelligent Transportation Systems and Safety, GITSS 2022
Y2 - 16 September 2022 through 18 September 2022
ER -