## 5 Conclusions  
In this paper, a SVR model was developed to make accurate driving decisions for autonomous vehicle. Our model was optimized by a weighted hybrid kernel function and a PSO algorithm. Road conditions and vehicle states were simultaneously as the reference indexes of DDM. The driving decisions that were made by DDM included free driving, car following, and lane changing. Then, driving simulated experiments with different traffic environments were executed to extract the driving decision samples. The optimized SVR model was trained and validated with the training and testing samples to establish DDM. Our model was compared with: (1) a SVR model with RBF kernel function, and (2) BPNN model. The comparison results showed that the accuracy of our optimized SVR model was the best, with more than 92% accuracy. Besides, the results also showed that our optimized SVR model had a better performance in free driving and car following with 93.1% and 94.7% of accuracy, respectively, than lane changing decision with 89.1% of accuracy.  
Finally, we investigated the effect of road conditions on the accuracy of DDM and quantified their effects on each driving decision through the sensitive analysis. The results showed that road conditions almost had almost no influence on driving decisions with high traffic density range, and had the greatest influence with low traffic density range. In the low and middle traffic density, road visibility \(\delta\) has the greatest effect on the driving decisions, then followed by \(\mu\), \(\rho\), and \(\tau\). To some extent, the verified results were consistent with the actual driving experience, which indicated the reasonability of the obtained DDM with added road conditions.  
Even though the DDM based on the optimized SVR model is able to reason driving decisions, and outperforms other models that are proposed in this paper, there are still some weak points and limits, such as the sample size of lane changing decision is smaller than that of car following and free driving, and that the DDM has not yet been implemented in real road environment, we will improve them in the future. In addition, future research will focus on establishing a DDM used in dangerous driving environments, for example, if a pedestrian or vehicle suddenly present in front of the subject vehicle, then the subject vehicle should make proper driving decision, like steering, braking, or steering and braking.  
**Acknowledgments:** This work was financially supported by the Natural Science Foundation of China under Grant no. 51678320, the Outstanding Young Scientists Research Award Foundation of Shandong Province under Grant no. ZR2016 EEB06, the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents under Grant no. 2015 RCJJ035.  
**Author Contributions:** The corresponding authors [PERSON] and [PERSON] proposed this research and designed the experiments; The corresponding authors [PERSON] and [PERSON] performed the experiments and analyzed the experimental data; [PERSON] was responsible to the analysis of model and drafted the manuscript with the help of [PERSON]. [PERSON] and [PERSON] proposed some amendment opinions and helped to improve this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.