Intelligent vehicle grading warning and braking system based on projection warning

doi:10.3969/j.issn.1674-8484.2025.03.006

Abstract

Abstract:

In order to improve the active safety performance of vehicles and reduce the number of human and vehicle collisions in the scene of blind area occlusion, an autonomous vehicle hierarchical early-warning braking system was proposed based on intelligent projection, focusing on the risk warning of smart car and pedestrian accidents.The hierarchical control strategy was used to model the braking system. The upper controller was a safety decision-making method for graded warning and braking systems, which explored the safe speed in the blind area through the change of the moving position of the vehicle and the pedestrian. The first stage braking was carried out according to the comparison between the safe speed in the blind area and the current speed, and the second stage braking was carried out when there was a collision risk with the pedestrian. The lower actuator established the inverse longitudinal dynamics model of the vehicle, and the throttle opening model and brake pressure control model of the vehicle were established according to the expected deceleration speed output of the upper controller, so as to maintain or decelerate the current speed. At the same time, the system used the road projection technology of smart cars to project warning information on the ground ahead, and carried out information interaction with pedestrians. The safety and feasibility of this system were verified by taking the traditional classic MAZDA model as the reference model. After 1 260 simulations, it was found that the hierarchical early-warning braking system did not avoid 14 accidents. For the unavoidable cases, 336 deep pedestrian interaction simulations were further conducted to simulate the real reactions of pedestrians after seeing the projected warnings. The results show that within the normal reaction capacity of pedestrians, the hierarchical early-warning braking system can avoid all accident cases. The research results can provide support for the research of automatic emergency braking system to protect pedestrian safety and promote its practical application.

Key words: intelligent vehicle, automative safety, stage warning brake system, blind speed, road projection

CLC Number:

U461.91

ZOU Tiefang, FU Xijun, LI Yanchun. Intelligent vehicle grading warning and braking system based on projection warning[J]. Journal of Automotive Safety and Energy, 2025, 16(3): 405-413.

Figures/Tables 13

References 28

[1]	World Health Organization. Global status report on road safety 2023: Summary[EB/OL]. [2024-06-18] https://www.who.int/publications/i/item/9789240086517.
[2]	袁泉, 胡孟夏, 李一兵. 基于真实事故数据的人车碰撞事故场景设计与驾驶模拟实验[J]. 汽车安全与节能学报, 2012, 3(1): 19-25.
	YUAN Quan, HU Mengxia, LI Yibing. Scenario design and driving simulation experiment of vehicle-to-pedestrain accidents based on real accident data[J]. J Autom Safe Energ, 2012, 3(1): 19-25. (in Chinese)
[3]	薛新风, 彭巍, 胡芷菁, 等. 湖南省智慧高速公路发展趋势及实践应用[J]. 公路与汽运, 2024, 40(5): 20-25.
	XUE Xinfeng, PENG Wei, HU Zhijing, et al. Development trend and practical application of smart expressway in Hunan Province[J]. Highw Autom Appl, 2024, 40(5): 20-25. (in Chinese)
[4]	Islam M, Rahman M, Chowdhury M, et al. Vision-based personal safety messages (PSMs) generation for connected vehicles[J]. IEEE Trans Vehi Tech, 2020, 69(9): 9402-9416.
[5]	王畅, 王一飞, 葛振振, 等. 全触屏人机交互模式下驾驶人视觉负荷与操作特性[J]. 汽车安全与节能学报, 2023, 14(4): 431-438.
	WANG Chang, WANG Yifei, GE Zhenzhen, et al. Driver visual load and operating characteristics under a human- machine interface mode with full-touch-screen[J]. J Autom Safe Energ, 2023, 14(4): 431-438. (in Chinese)
[6]	Morales L Y, Naoki A, Yoshihara Y, et al. Towards predictive driving through blind intersections[C]// 2018 21st Int’l Conf Intel Transport Syst (ITSC). IEEE, 2018: 716-72.
[7]	徐新东, 何广进, 树爱兵, 等. 面向典型信号控制场景的路段行人过街特性研究[J]. 公路与汽运, 2023(3): 33-39.
	XU Xindong, HE Guangjin, SHU Aibing, et al. Research on pedestrian crossing characteristics for typical signal control scenarios[J]. Highw Autom Appl, 2023(3): 33-39. (in Chinese)
[8]	QU Dayi, LI Haiyang, LIU Haomin, et al. Crosswalk safety warning system for pedestrians to cross the street intelligently[J]. Sustainability, 2022, 14(16): 10223.
[9]	Keller C G, Dang T, Fritz H, et al. Active pedestrian safety by automatic braking and evasive steering[J]. IEEE Trans Intel Transport Syst, 2011, 12(4): 1292-1304.
[10]	Lee H K, Shin S G, Kwon D S. Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion[J]. Int’l J Autom Tech, 2017, 18(6): 1067-1076.
[11]	ZHANG Lin, YUAN Jiawei, YUAN Kang, et al. Automated braking decision and control for pedestrian collision avoidance based on risk assessment[J]. IEEE Intel Transport Syst Maga, 2022, 14(3): 41-61.
[12]	兰凤崇, 余蒙, 李诗成, 等. 考虑预碰撞时间的自动紧急制动系统分层控制策略研究[J]. 汽车工程, 2020, 42(2): 206-214.
	LAN Fengchong, YU Meng, LI Shicheng, et al. Research hierarchical control strategy for automatic emergency braking system with consideration of time-to-collision[J]. Autom Engineering, 2020, 42(2): 206-214. (in Chinese)
[13]	Narksri P, Takeuchi E, Ninomiya Y, et al. Deadlock-free planner for occluded intersections using estimated visibility of hidden vehicles[J]. Electronics, 2021, 10(4): 411-423.
[14]	NIE Bingbing, LI Quan, GAN Shun, et al. Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behavior[J]. Sci Reports, 2021, 11(1): 3996.
[15]	邹铁方, 刘前程, 魏亮. 加装传统AEB后的未避免事故典型碰撞场景与事故特征[J]. 汽车工程, 2023, 45(6): 1062-1072.
	ZOU Tiefang, LIU Qingchen, WEI Liang. Typical collision scenarios and accident characteristics of accidents not avoided after installing traditional AEB[J]. Autom Engineering, 2023, 45(6): 1062-1072. (in Chinese)
[16]	ZOU Tiefang, LIU Zhuzi, WANG Dangi, et al. Assessing the effect of pedestrian deceleration on pedestrian autonomous emergency braking systems[J]. Int’l J Crashworth, 2021, 27(5): 1368-1373.
[17]	TONG Yourui, JIA Bochen, BAO Shan. An augmented warning system for pedestrians: user interface design and algorithm development[J]. Appl Sci, 2021, 11(16): No 7197.
[18]	McLaughlin S B. Analytic assessment of collision avoidance systems and driver dynamic performance in rear-end crashes and near-crashes[D]. Virginia: Virginia Polytechnic Institute and State University, 2008.
[19]	谈东奎, 胡港君, 朱波, 等. 考虑预期功能安全的智能汽车自动紧急制动系统[J]. 汽车工程, 2022, 44(6): 799-807, 830.
	TAN Dongkui, HU Gangjun, ZHU Bo, et al. Intelligent vehicle autonomous emergency braking system considering safety of the intended functionality[J]. Autom Engineering, 2022, 44(6): 799-807, 830. (in Chinese)
[20]	Bengler K, Rettenmaier M, Fritz N, et al. From HMI to HMIs: Towards an HMI framework for automated driving[J]. Info, 2020, 11(2): 61-78.
[21]	Bazilinskyy P, Dodou D, DE Winter J. Survey on eHMI concepts: The effect of text, color, and perspective[J]. Transport Res Part F: Traf Psych Behav, 2019, 67: 175-194.
[22]	Han D, Kwak S S, Park J, et al. On-road dynamic pattern projection using an active mirror for vehicle warning systems[J]. IEEE Trans Vehi Tech, 2020, 69(5): 4947-4956.
[23]	LI Quan, SHANG Shi, PEI Xizhe, et al. Kinetic and kinematic features of pedestrian avoidance behavior in motor vehicle conflicts[J]. Front Bioengi Biotech, 2021, 9: No 783003.
[24]	Doi A, Butsuen T, Niibe T, et al. Development of a rear-end collision avoidance system with automatic brake control[J]. JSAE Rev, 1994, 15(4): 335-340.
[25]	李迎弟. 汽车自动紧急制动系统控制策略研究[D]. 西安: 长安大学, 2020.
	LI Yidi. Research on the control strategy of autonomous emergency braking system[D]. Xi’an: Chang’an University, 2020. (in Chinese)
[26]	杨娜, 张臻, 赵桂范, 等. 自动制动系统行人保护效果的研究[J]. 汽车工程, 2015, 37(12): 1400-1405, 1394.
	YANG Na, ZHANG Zhen, ZHAO Guifang, et al. A Research on the pedestrian protection effects of automatic braking system[J]. Autom Engineering, 2015, 37(12): 1400-1405,1394. (in Chinese)
[27]	ZOU Tiefang, Aiming Z, LIU Qi, et al. Pedestrian gaits observed from actual pedestrian-vehicle collisions[J]. Int’l J Crashworth, 2022, 27(1): 1-23.
[28]	Abdel-Aty M, CAI Qing, WU Yina, et al. Evaluation of automated emergency braking system’s avoidance of pedestrian crashes at intersections under occluded conditions within a virtual simulator[J]. Accid Anal Prevent, 2022, 176: No 106797.

盲区仿真参数	数值
盲区长×宽，l×w	4.8 m×2.1 m
车速，v_x	60 km/h
车辆一级制动减速度，a_x	0.4 g m/s²
行人横向坐标，d₁	5.25 m
行人速度，v_p	2 m/s
行人减速度，a_r	-4 m/s²
城市道路限速，v_max	60 km/h
最低车速，v_min	20 km/h

盲区仿真参数	数值
盲区长×宽，l×w	4.8 m×2.1 m
车速，v_x	60 km/h
车辆一级制动减速度，a_x	0.4 g m/s²
行人横向坐标，d₁	5.25 m
行人速度，v_p	2 m/s
行人减速度，a_r	-4 m/s²
城市道路限速，v_max	60 km/h
最低车速，v_min	20 km/h

编号	v_p / (m·s^-1)	d_z / m	T_p / s	数量
1	1.0	1	4	1
2	2.0	1	5	1
3	2.0	2	3	1
4	2.5	1	3	1
5		2	4	1
6		3	4	1
7	3.0	1	5	1
8	3.0	1	4	1
9	3.5	1	5	1
10		2	5	1
11		2	5	1
12	4.0	1	5	2
13	4.0	2	5	1
总计				14

编号	v_p / (m·s^-1)	d_z / m	T_p / s	数量
1	1.0	1	4	1
2	2.0	1	5	1
3	2.0	2	3	1
4	2.5	1	3	1
5		2	4	1
6		3	4	1
7	3.0	1	5	1
8	3.0	1	4	1
9	3.5	1	5	1
10		2	5	1
11		2	5	1
12	4.0	1	5	2
13	4.0	2	5	1
总计				14