Abstract
Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.
Highlights
With the current rapid economic growth, vehicle ownership is fast increasing, accompanied by more than one million traffic accidents per year worldwide
As an important manifestation of the intelligent level of autonomous vehicles, the driving decision-making has currently become the focus and difficulty for experts in the study of autonomous vehicle [3]. It needs to rely on driving decision-making mechanism (DDM) to decide accurate driving strategy [4]
Our model was optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm
Summary
With the current rapid economic growth, vehicle ownership is fast increasing, accompanied by more than one million traffic accidents per year worldwide. In order to alleviate traffic accidents, autonomous vehicles have been the world’s special attention for its non-driver’s participation. As an important manifestation of the intelligent level of autonomous vehicles, the driving decision-making has currently become the focus and difficulty for experts in the study of autonomous vehicle [3]. For autonomous vehicle, it needs to rely on driving decision-making mechanism (DDM) to decide accurate driving strategy [4]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have