Research on Intelligent Car Interface Interaction Optimization Algorithm Based on User Experience Human Factors Engineering

This study proposes an intelligent car interface interaction optimization algorithm based on user experience human factors engineering. The algorithm combines the basic principles of human factors engineering, designs the interface interaction of intelligent cars, and adopts deep reinforcement learning (DRL) technology to adapt to diverse user needs and complex driving scenarios. By collecting and analyzing the interactive behaviors of users in different situations, the algorithm can adaptively adjust the interface layout, information presentation and interaction mode to improve the overall user experience. Through simulation verification, the experimental results show that the optimization algorithm can effectively improve the user's operating experience in a variety of driving scenarios, significantly reducing the user's cognitive load, and improving the interaction efficiency. Simulation data analysis shows that the interface response time after optimization is reduced by an average of 23.5%, and user satisfaction is increased by 15.8%. The results of this study provide new ideas for the design and optimization of intelligent car interface interaction, and lay the foundation for the optimization of human-computer interaction in future intelligent driving systems.