Driver Intention Recognition

Driver intention recognition aims to predict driver actions, such as lane changes, using data from vehicle sensors and driver behavior. Current research heavily utilizes deep learning architectures, including recurrent neural networks and transformers, often exploring optimal network designs and data fusion techniques to improve prediction accuracy. Accurate driver intention prediction is crucial for enhancing the safety and efficiency of autonomous driving systems and improving overall road safety. The field is actively investigating the relationship between model complexity and performance, seeking to optimize algorithms for real-world deployment.