Sage Journals: Discover world-class research

Abstract

Aiming at the lane decision problem of autonomous driving (AD) in normal driving scenarios on structured road, with the goal of high safety and high efficiency, a lane decision algorithm for autonomous vehicles (AVs) considering potential driving risks is proposed. In this algorithm, by simulating the dimensions of human drivers’ attention to the driving environment (DE), the DE of AV is characterized from uncertainty, driving efficiency, and regional driving risk, based on the proposed potential risk field model, right-of-way (ROW) field model, and regional performance risk field model. Furthermore, by mining the behavioral patterns of human drivers’ lane decision based on their natural driving behavior data, the proposed algorithm has the ability to simulate the human drivers’ lane decision behavior. This is beneficial for integrating AVs into practical driving scenarios and will not cause discomfort to surrounding vehicles (SVs). In a continuous complex dynamic scenario composed of eight sub-scenarios, the performance verification based on two baseline algorithms and the performance comparison with six advanced algorithms are carried out. The results show that the proposed algorithm can balance driving safety and efficiency and select an optimal lane for AV.

Keywords

autonomous driving lane decision potential risk performance risk right-of-way

Get full access to this article

View all access options for this article.

References

Claussmann

Revilloud

Gruyer

, et al. A review of motion planning for highway autonomous driving. IEEE Trans Intell Transp Syst 2019; 21(5): 1826–1848.

Yurtsever

Lambert

Carballo

, et al. A survey of autonomous driving: common practices and emerging technologies. IEEE Access 2020; 8: 58443–58469.

Yang

Lyu

Dai

L Y

, et al. Decision-making model for lane selection of automated vehicles in connected vehicle environment. China J Highw Transp 2022; 35(4): 243–255.

Feng

Wei

Zhao

, et al. Research on lane-changing decision making and planning of autonomous vehicles based on GCN and multi-segment polynomial curve optimization. Sensors 2024; 24(5): 1439.

Wang

Zheng

, et al. Driving safety field theory modeling and its application in pre-collision warning system. Transp Res C Emerg Technol 2016; 72: 306–324.

Xiong

Zhong

, et al. Rule-guidance reinforcement learning for lane change decision-making: a risk assessment approach. Chin J Mech Eng 2025; 38(1): 30.

Wang

Jiang

, et al. Enhancing lane change safety and efficiency in autonomous driving through improved reinforcement learning for highway decision-making. Electronics 2025; 14(5): 918.

Pei

Chen

, et al. A safe and efficient lane change decision-making strategy of autonomous driving based on deep reinforcement learning. Mathematics 2022; 10(9): 1551.

Han

Wang

, et al. Safe and efficient DRL driving policies using fuzzy logic for urban lane changing scenarios. J Intell Connect Veh 2025; 8(1): 1–13.

10.

Fang

Tianhe

Weixing

Servo-level human–machine shared control flexible strategy based on driving ability, status and regionalized environmental risk. IEEE Trans Transp Electrif 2023; 9(3): 4418–4436.

11.

State Administration for Market Regulation, Standardization Administration of China. Performance requirements and test methods for advanced emergency braking system (AEBS) of commercial vehicles: GB/T 38186-2019. Beijing: Standards Press of China, 2019.

12.

Zhang

Yang

, et al. A review of traffic operation risk analysis on highways in a connected and automated environment. J Transp Inf Saf 2025; 43(2): 1–22.

13.

Chen

Wang

, et al. Safety reliability evaluation when vehicles turn right from urban major roads onto minor ones based on driver’s visual perception. Accid Anal Prev 2016; 95: 487–494.

14.

Insurance Institute for Highway Safety (IIHS). Autonomous vehicles and crash prevention: insights from real-world data report (R). Insurance Institute for Highway Safety (IIHS), 2020.

15.

Martinez

Heucke

Wang

, et al. Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans Intell Transp Syst 2017; 19(3): 666–676.

16.

Liu

Xue

Wang

, et al. Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving. Green Energy Intell Transp 2023; 2(2): 100065.

17.

Krajewski

Bock

Kloeker

, et al. The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 21st international conference on intelligent transportation systems (ITSC), Maui, HI, 2018, pp. 2118–2125. IEEE.

18.

Yin

Wang

, et al. Driving fatigue quantization based on entropy weight method. J South China Univ Technol Nat Sci Ed 2017; 45(8): 50–56.

19.

Davison

Smith

RL.

Models for extreme values: a review. Stat Sci 1990; 5(4): 395–413.

20.

Zhao

Yang

YB.

Man-machine shared driving model using risk-response mechanism of human driver. J Jilin Univ Eng Technol Ed 2021; 51(3): 799–809.

21.

Tian

Research on traffic hazard perception of urban expressway drivers based on eye movement characteristics. Dissertation, Chang’an University, China, 2024.

22.

Fang

Xiaowen

Weixing

, et al. Dynamic path-speed planning algorithm for autonomous driving on structured roads. Proc Inst Mech Eng D J Automob Eng 2024; 238(10–11): 3172–3193.

23.

Zhang

Chang

Zeng

, et al. Multi-agent DRL-based lane change with right-of-way collaboration awareness. IEEE Trans Intell Transp Syst 2022; 24(1): 854–869.

24.

Liu

Song

X-y.

An applicable multivariate decision tree algorithm for categorical attribute data. J Northeast Univ Nat Sci 2020; 41(11): 1521–1527.

25.

Huang

Liu

, et al. ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning. Meas Sci Technol 2024; 35(12): 126312.

26.

Chen

Tang

, et al. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mech Syst Signal Process 2018; 100: 482–500.

A lane decision algorithm for autonomous vehicles considering potential driving risks

Abstract

Keywords

Get full access to this article

References