Key Takeaways & Executive Findings
- •• Proposes a deep reinforcement learning-based multi-objective parallel human-machine steering coordination strategy for path tracking, addressing driver misoperation and external disturbances. • Integrates a driver steering model with Tube MPC for robust path tracking, and employs DQN, DDPG, and TD3 algorithms to optimize coordination. • Demonstrates via simulations and hardware-in-the-loop experiments that the TD3-based strategy outperforms fuzzy and other DRL methods in tracking accuracy, lateral safety, and reduced human-machine conflict. • Provides a comprehensive evaluation framework with multiple indices (tracking accuracy, lateral safety, human-machine conflict, driver steering load) under varied driving conditions.
Abstract
In the parallel steering coordination control strategy for path tracking, it is difficult to match the current driver steering model using the fixed parameters with the actual driver, and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’ input. In this paper, we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance. Firstly, the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response, and the driver characteristic parameters are fitted after collecting the actual driver driving data. Secondly, considering that the vehicle is susceptible to the influence of external disturbances during the driving process, the Tube MPC (Tube Model Predictive Control) based path tracking steering controller is designed based on the vehicle system dynamics error model. After verifying that the driver steering model meets the driver steering operation characteristics, DQN (Deep Q-network), DDPG (Deep Deterministic Policy Gradient) and TD3 (Twin Delayed Deep Deterministic Policy Gradient) deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’ input of the vehicle. Finally, the tracking accuracy, lateral safety, human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments, and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments. The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation, and the TD3-based coordination control strategy has better overall performance.
1. Introduction
Due to the immaturity of current science and technology levels and related humanities and laws, fully autonomous driving will not become a human travel way in a short time, and human-machine co-driving receives wide attention as an important transition way to realize autonomous driving [1]. Considering the driver in the loop, the steering coordination control strategy to assist the driver in path tracking is an important research direction in human-machine co-driving technologies. By coordinating the relationship in steering between the driver and the controller, the human-machine steering coordination control strategy can improve the driving performance of intelligent vehicles in the path tracking process while keeping the human-machine conflict as small as possible. In addition, by allocating the drive rights of vehicles reasonably, the human-machine steering coordination control strategy can relieve the driver steering load to a certain extent, which is beneficial for the occurrence of traffic accident reduction.
Steering coordination mainly includes haptic guidance control and shared steering control. The haptic guidance control strategy assists the driver in steering operations by applying torque to the steering wheel, and the driver can directly feel the assistant torque acting on the steering wheel [2, 3]. A haptic guidance steering coordination control strategy was designed under cornering conditions in Ref. [4]; by combining the driver input torque, a real-time varying scale factor was designed, and the controller calculated the corresponding assistant torque based on the vehicle-road interaction information. Yan et al. designed an adaptive proportional control algorithm based on whether the driver and the controller p
Loading authentic research manuscript (Pages 1–5)...
Hongbo Wang, Lizhao Feng, Shaohua Li, Wuwei Chen, Juntao Zhou (2025). Multi-Objective Parallel Human-machine Steering Coordination Control Strategy of Intelligent Vehicles Path Tracking Based on Deep Reinforcement Learning. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01207-9
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a deep reinforcement learning-based multi-objective parallel human-machine steering coordination strategy for path tracking, which considers driver misoperation and external disturbances, and demonstrates superior performance over fuzzy logic and other DRL algorithms.
Which deep reinforcement learning algorithms are compared in the study?
The study compares DQN (Deep Q-network), DDPG (Deep Deterministic Policy Gradient), and TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithms for the coordination strategy.
How is the driver steering model constructed?
The driver steering model is constructed based on driver preview characteristics and steering delay response, with parameters fitted using actual driver driving data.
What are the evaluation indices used in the experiments?
The evaluation indices include tracking accuracy, lateral safety, human-machine conflict, and driver steering load, tested under different driver operation states and road environments.
What is the significance of the TD3-based coordination control strategy?
The TD3-based strategy shows better overall performance in assisting drivers with path tracking under lateral wind interference and driver misoperation, outperforming fuzzy and other DRL-based strategies.
Related Technical Papers & Translations
Direct Repair of the Crystal Structure and Coating Surface of Spent LiFePO4 Materials Enables Superfast Li-Ion Migration
The rapid accumulation of spent LiFePO4 (LFP) cathodes from retired lithium-ion batteries necessitates the development of effective and environmental-friendly recycling strategies. In this context, direct regeneration has emerged as a promising approach for reclaiming LFP cathode materials, offering a streamlined pathway to restore their electrochemical functionality. We report an integrated regeneration protocol that simultaneously repairs the degraded crystal structure and reconstructs the damaged carbon coating in spent LFP. The regenerated cathode material had superfast lithium-ion diffusion kinetics and a stable cathode–electrolyte interface, giving a remarkable rate capability with specific capacities of 122 mAh g−1 at 5C and 106 mAh g−1 at 10C (1C = 170 mA g−1). It also maintained capacities of 110.7 mAh g−1 (5C) and 84.1 mAh g−1 (10C) after 400 cycles. It could be used in harsh environments and could be stably cycled at subzero temperatures (−10 and −20 °C) and in solid-state electrolyte batteries. Life cycle assessment combined with economic evaluation using the EverBatt model reveals that this direct regeneration approach has high economic and environmental benefits.
Oxide Semiconductor for Advanced Memory Architectures: Atomic Layer Deposition, Key Requirement and Challenges
Oxide semiconductors (OSs), introduced by the Hosono group in the early 2000s, have evolved from display backplane materials to promising candidates for advanced memory and logic devices. The exceptionally low leakage current of OSs and compatibility with three-dimensional (3D) architectures have recently sparked renewed interest in their use in semiconductor applications. This review begins by exploring the unique material properties of OSs, which fundamentally originate from their distinct electronic band structure. Subsequently, we focus on atomic layer deposition (ALD), a core technique for growing excellent OS films, covering both basic and advanced processes compatible with 3D scaling. The basic surface reaction mechanisms—adsorption and reaction—and their roles in film growth are introduced. Furthermore, material design strategies, such as cation selection, crystallinity control, anion doping, and heterostructure engineering, are discussed. We also highlight challenges in memory applications, including contact resistance, hydrogen instability, and lack of p-type materials, and discuss the feasibility of ALD-grown OSs as potential solutions. Lastly, we provide an outlook on the role of ALD-grown OSs in memory technologies. This review bridges material fundamentals and device-level requirements, offering a comprehensive perspective on the potential of ALD-driven OSs for next-generation semiconductor memory devices.
Laser powder bed fusion of biodegradable Zn-4Cu alloy: Processing, microstructure and properties
Zn's natural degradability and biocompatibility make it a promising candidate for implants, however, its mechanical properties remain insufficient for bone applications. In this study, the performance of Zn was enhanced by developing Zn-Cu alloys via laser powder bed fusion (LPBF). Optimal LPBF parameters for forming stable tracks were achieved by adjusting laser power and scanning speed. Under optimized conditions of 100 W and 100 mm/s, high-density (99.58%) Zn-Cu alloys with improved hardness (68.2HV) and yield strength (160 MPa) were achieved. These improvements are attributed to solid solution strengthening, segregation strengthening, and grain refinement. The Zn-Cu alloys also demonstrated favorable degradation behavior, with a rate of 0.16 mm/year. This degradation is primarily driven by micro-galvanic corrosion between the CuZn5 phase and Zn matrix, along with refined grains and increased grain boundary density. This work demonstrates a viable strategy for fabricating Zn-based implants with enhanced structural integrity and mechanical performance via LPBF.