Academic Journal of Engineering and Technology Science, 2025, 8(4); doi: 10.25236/AJETS.2025.080409.
Hao Tang
Sichuan Yiyun Intelligent Connected Vehicle Technology Co., Ltd., Yibin, Sichuan, China
Against the backdrop of rapid development in artificial intelligence and sensor technology, autonomous vehicles are steadily moving toward practical application. An efficient, natural, and safe human-machine interaction (HMI) system has become the core for gaining user trust and achieving wide-scale adoption. Focusing on the specific scenario of L4-level autonomous driving, this study proposes an HMI framework integrating multimodal perception, adaptive decision-making, and cognitive load optimization. It particularly examines deep learning-based intent recognition algorithms, dynamic optimization strategies for takeover requests in emergency situations, and multi-channel fusion mechanisms for presenting interactive information. Through high-fidelity driving simulators and real-vehicle road tests, combined with eye-tracking, physiological signal monitoring, and other relevant methods, the system’s effectiveness in reducing cognitive load, enhancing situational awareness, and shortening takeover reaction time was validated. Data show that the optimized HMI reduces average driver takeover reaction time by 42.7%, increases situational understanding accuracy to 91.3%, and significantly lowers subjective workload index. This research provides key technical support for building a highly usable and safe human-machine co-driving paradigm for autonomous vehicles and holds great significance for promoting the commercialization of autonomous driving.
Autonomous Vehicles; Human-Machine Interaction Design; Multimodal Interaction; Cognitive Load; Takeover Performance; Reinforcement Learning; Eye-Tracking
Hao Tang. Design of Human-Machine Interaction for Autonomous Vehicles in the Era of Intelligence. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 4: 67-71. https://doi.org/10.25236/AJETS.2025.080409.
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