Key Takeaways & Executive Findings
- •• Introduces a novel multi-mode evasion assistance control (MEAC) method that coordinates collision avoidance safety with human-machine interaction. • The S-EAC mode uses an integrated authority allocation mechanism to reduce human-machine conflict during shared control. • The E-EAC mode employs model predictive control to generate additional yaw moments for emergency collision avoidance when the driver is unresponsive. • Simulation and experiments demonstrate improved safety, stability, and reduced emergency steering in both modes.
Abstract
Vehicle collision avoidance (CA) has been widely studied to improve road traffic safety. However, most evasion assistance control methods face challenges in effectively coordinating collision avoidance safety and human-machine interaction conflict. This paper introduces a novel multi-mode evasion assistance control (MEAC) method for intelligent distributed-drive electric vehicles. A reference safety area is established considering the vehicle safety and stability requirements, which serves as a guiding principle for evading obstacles. The proposed method includes two control modes: Shared-EAC (S-EAC) and Emergency-EAC (E-EAC). In S-EAC, an integrated human-machine authority allocation mechanism is designed to mitigate conflicts between human drivers and the control system during collision avoidance. The E-EAC mode is tailored for situations where the driver has no collision avoidance behavior and utilizes model predictive control to generate additional yaw moments for collision avoidance. Simulation and experimental results indicate that the proposed method reduces human-machine conflict and assists the driver in safe collision avoidance in the S-EAC mode under various driver conditions. In addition, it enhances the vehicle responsiveness and reduces the extent of emergency steering in the E-EAC mode while improving the safety and stability during the collision avoidance process.
1. Introduction
There are almost 1.19 million road traffic deaths every year globally, where collision accidents account for about 98% [1, 2]. Intelligent vehicles, incorporating multi-source sensor systems and advanced control algorithms, have emerged as a promising solution to improve road safety [3, 4]. On this basis, collision avoidance (CA) algorithms have been widely studied and the corresponding technologies such as forward collision warning (FCW), automatic emergency braking (AEB), autonomous emergency steering (AES), etc. are used in ADAS in production vehicles [5–7].
Longitudinal CA system generates the desired deceleration through the braking system and avoids collision or reduces the degree of collision by lowering the vehicle’s speed. The decision logic and control execution are relatively simple, which essentially controls the speed difference or distance difference between the ego vehicle (EV) and the obstacle vehicle (OV) [8]. Lateral CA system is to generate enough lateral displacement through the steering system to minimize the lateral overlap between the EV and the OV [9].
However, relying solely on longitudinal braking has inherent limitations in evasion. Ref. [10] evaluates AEB systems in six commercially available vehicles and shows that they could only ensure successful CA at speeds below 50 km/h. From the study of Continental, it can be more intuitively found that the longitudinal CA system is usually able to safely avoid collisions at low relative speeds, while the lateral CA system is able to avoid crashes more efficiently at medium and high relative speeds [11]. Figure 1 schematically shows the relationship between the distance required for CA and the relative vehicle speed, including the distance of the last point to brake (LPTB) and the distance of the last point to steer (LPTS) [12]. As the relative speed increases, the LPTB distance gradually approaches and exceeds the LPTS distance. For conditions where the relative speed is above 45 km/h, it is easier to accomplish CA by using lateral CA methods.
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Bo Leng, Zhuoren Li, Ming Liu, Ce Yang, Yi Luo, Amir Khajepour, Lu Xiong (2025). Multi-mode Evasion Assistance Control Method for Intelligent Distributed-drive Electric Vehicle Considering Human Driver's Reaction. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01270-2
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel multi-mode evasion assistance control (MEAC) method for intelligent distributed-drive electric vehicles, which includes two modes (S-EAC and E-EAC) to coordinate collision avoidance safety and human-machine interaction conflict.
How does the S-EAC mode work?
In S-EAC mode, an integrated human-machine authority allocation mechanism is designed to mitigate conflicts between human drivers and the control system during collision avoidance, allowing shared control.
What is the purpose of the E-EAC mode?
The E-EAC mode is tailored for situations where the driver has no collision avoidance behavior. It uses model predictive control to generate additional yaw moments for collision avoidance, enhancing vehicle responsiveness and reducing emergency steering.
What are the key findings of the study?
Simulation and experimental results show that the proposed method reduces human-machine conflict and assists the driver in safe collision avoidance in S-EAC mode, while in E-EAC mode it improves safety and stability during collision avoidance.
What is the significance of this research?
This research addresses the challenge of coordinating collision avoidance safety with human-machine interaction, offering a practical solution for intelligent distributed-drive electric vehicles to improve road safety.
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