Vehicle Motion Prediction Algorithm with Driving Intention Classification
Source abstract
Abstract
The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle’s safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver’s driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle emissions and performance · Autonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Antobot Robot (Shanghai) Limited, Shanghai 201800, China
School of Automotive Studies, Tongji University, Shanghai 201800, China
Sino-German School for Post-Graduate Studies, Tongji University, Shanghai 201800, China
References (36)
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