Review on driver intention recognition(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2022年1期
- Page:
- 33-60
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Review on driver intention recognition
- Author(s):
- FU Rui1; 2; ZHANG Hailun1; LIU Wenxiao1; ZHANG Hongjia
- 1. School of Automobile, Changan University, Xian 710064, Shaanxi, China; 2. Key Laboratory ofAutomotive Transportation Safety Technology, Ministry of Transport,Changan University, Xian 710064, Shaanxi, China
- Keywords:
- traffic engineering; driver intention; overview; driving behavior; recognition model
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to fully understand the research progress of drivers intention recognition, the research on drivers intention recognition in the past 30 years was sorted out. Drivers intention was classified into strategic intention, tactical intention and operation intention. According to the research hotspots, the intention of changing lanes, turning, braking and overtaking were mainly summarized. The structure, input, algorithm and evaluation of the drivers intention recognition system from the perspective of system construction was summarized. According to the different input of the system, the four driving intentions were summarized from the traffic environment, vehicle movement and driver behavior. According to the different algorithms used to construct the model, the research on the driving intention recognition model was reviewed from six aspects, such as generative model, discriminant model, deep learning, cognitive model, rulebased decision and semisupervised learning model. The results show that the vehicle dynamic information can not be used as input information to predict the drivers intention in general, but it can be used as an effective indicator to detect the drivers early intention after the vehicle maneuver has started. Traffic environment and driver behavior signals are very effective in predicting lane changing, braking and overtaking intentions, but the input as steering intention prediction is not reliable, and the vehicle trajectory can better reflect the drivers steering intention. Appropriate parameters should be selected to construct different drivers intention recognition models. Existing driver intention recognition models constructed using machine learning, including deep learning methods, have limitations such as poor model interpretation, sensitivity to data samples, and poor scalability. The rule judgment model can not adapt to the changing road environment and driving style. The drivers intention recognition model should provide humancentered technical support for the development of autonomous driving technology. It can monitor the drivers state and situational awareness of the traffic environment, capture the drivers perception and cognitive characteristics, and adopt semisupervised learning methods to improve model robustness and reduce model development time. Before the connected traffic environment is formed, the drivers intention recognition model in the mixed connected scenario needs to be studied in depth. 4 tabs, 13 figs, 145 refs.
Last Update: 2022-04-06