Intelligent Driving Assistance System for Safe Expressway Driving in Rainy and Foggy Weather based on IoT

doi:10.1007/s12204-023-2564-4

Abstract

Abstract: The feature bends and tunnels of mountainous expressways are often affected by bad weather, specifically rain and fog, which significantly threaten expressway safety and traffic efficiency. In order to solve this problem, a vehicle–road coordination system based on the Internet of Things (IoT) is developed that can share vehicle–road information in real time, expand the environmental perception range of vehicles, and realize vehicle–road collaboration. It helps improve traffic safety and efficiency. Further, a vehicle–road cooperative driving assistance system model is introduced in this study, and it is based on IoT for improving the driving safety of mountainous expressways. Considering the influence of rain and fog on driving safety, the interaction between rainfall, water film, and adhesion coefficient is analyzed. An intelligent vehicle–road coordination assistance system is constructed that takes in information on weather, road parameters, and vehicle status, and takes the stopping sight distance model as well as rollover and sideslip model as boundary constraints. Tests conducted on a real expressway demonstrated that the assistance system model is helpful in bad weather conditions. This system could promote intelligent development of mountainous expressways.

Key words: rainy and foggy weather, expressway, driving assistance system, Internet of Things (IoT)

摘要： 山区高速公路不良天气频发，弯段多，且隧道多，雨雾区域性较强，极大影响了交通安全及通行效率。基于物联网的车路协同系统可以实时共享车路信息，扩大车辆的环境感知范围，实现车路协同工作，从而保障交通安全，提高通行效率。因此，针对雨雾天高速公路行车安全问题，本文提出了基于物联网的车路协同行车辅助系统模型。基于雨雾的影响原理，分析降雨，水膜及附着系数方面的作用机理。以天气信息、道路参数、车辆状态为系统输入，以停车视距、防侧翻侧滑为边界约束，构建面向智能交通的车路协同行车辅助系统。与不良天气下某实际高速公路的实车实验表明：该辅助系统有利于提升不良天气下行车安全，支持提升山区高速公路智能化发展水平。

关键词: 雨雾天气，高速公路，行车辅助系统，物联网

CLC Number:

U49
U421.3

YAN Beirui (燕北瑞), FANG Cheng (方成), QIU Hao (邱昊), ZHU Wenfeng∗ (朱文峰). Intelligent Driving Assistance System for Safe Expressway Driving in Rainy and Foggy Weather based on IoT[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 10-19.

References

[1] BAI Y Q, HE M Q, LIU J, et al. Study on the relationship between highway traffic accidents and meteorological conditions [J]. Meteorological and Environmental Sciences, 2015, 38(2): 66-71 (in Chinese).
[2] CHEN C, ZHAO X H, LIU H, et al. Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations [J]. Journal of Modern Transportation, 2019, 27(4): 282-292.
[3] HAN S, XU J L, YAN M H, et al. Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities [J]. PLoS ONE, 2021, 16(7): e0252767.
[4] LEE J, CHAE J, YOON T, et al. Traffic accident severity analysis with rain-related factors using structural equation modeling - A case study of Seoul City [J]. Accident Analysis & Prevention, 2018, 112: 1-10.
[5] LUO W T, LI L. Development of a new analytical water film depth (WFD) prediction model for asphalt pavement drainage evaluation [J]. Construction and Building Materials, 2019, 218: 530-542.
[6] ZHOU Q. Study on the theoretical calculation of the thickness of water film on road surface and its effect on the pavement skid resistance condition [D]. Nanjing: Nanjing Forestry University, 2013 (in Chinese).
[7] NOVIKOV I, LAZAREV D. Experimental installation for calculation of road adhesion coefficient of locked car wheel [J]. Transportation Research Procedia, 2017, 20: 463-467.
[8] ABDULHAFEDH A. Highway stopping sight distance, decision sight distance, and passing sight distance based on AASHTO models [J]. Open Access Library Journal, 2020, 7(3): 1-24.
[9] BELLINI D, IACONIS M C, TRAETTINO E. Speed limits and road warning signs as aid for driving behavior [J]. Transportation Research Procedia, 2020, 45: 135-142.
[10] MA H, XU J L. The speed limit determination of tunnel entrance and exit section on rainy days [J]. IOP Conference Series: Earth and Environmental Science, 2021, 634(1): 012137.
[11] GUO Y F, CHENG G Z. Theoretical calculation of the maximum speed limit value on freeway in adverse weather [J]. Applied Mechanics and Materials, 2012, 209/210/211: 663-666.
[12] LI C C．Research on expressway traffic flow characteristics traffic guidance and control under adverse weather [D]. Beijing: Beijing University of Technology, 2015 (in Chinese)．
[13] QI F. Road traffic safety guidance system in fog area [J]. Electronic Technology & Software Engineering, 2019(2): 75 (in Chinese).
[14] ZHAO S, AL-QADI I. Pavement drainage pipe condition assessment by GPR image reconstruction using FDTD modeling [J]. Construction and Building Materials, 2017, 154: 1283-1293.
[15] CHU L J, FWA T F. Incorporating pavement skid resistance and hydroplaning risk considerations in asphalt mix design [J]. Journal of Transportation Engineering, 2016, 142(10): 04016039.
[16] CHEN X B, WANG J T, LIU H, et al. Influence of rainfall on skid resistance performance and driving safety conditions of asphalt pavements [J]. Journal of Southeast University (English Edition), 2019, 35(4): 482-490.
[17] JI T J, HUANG X M, LIU Q Q. Part hydroplaning effect on pavement friction coefficient [J]. Journal of Traffic and Transportation Engineering, 2003, 3(4): 10-12 (in Chinese).
[18] LI Z P, LIU Y C. A model of car-following with adaptive headway [J]. Journal of Shanghai Jiao Tong University (Science), 2006, 11(3): 394-398.
[19] ZHUANG H Y, QIAN Y Q, YANG M. Intelligent connected vehicle as the new carrier towards the era of connected world [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 559-560.
[20] LIU S Y, LI D W, XI Y G, et al. A short-term traffic flow forecasting method and its applications [J]. Journal of Shanghai Jiao Tong University (Science), 2015, 20(2): 156-163.