## 4 Conclusions  
To decrease the false detection rate of collisions with vehicles on winding roads and the missed detection rate of collisions with maneuvering vehicles, this article introduced an intent-estimation- and motion-model-based collision avoidance method for autonomous vehicles in urban environments. The maneuvering intent of detected vehicles is estimated by combining the vehicles' moving state and the road structure. A GMM is used to train the intent predictor and then used to predict the intent of detected vehicles. With the driving intent, an ideal long-term trajectory can be obtained by making a balance between timeliness and comfort. The short-term trajectory can be obtained by utilizing a constant yaw rate and acceleration motion model. Then, the long-term trajectory and the short-term trajectory are combined by generating a linear fit to predict an accurate trajectory. Finally, potential collisions are detected and can be avoided in the space dimension or in the time dimension by taking evasive actions in advance. The experimental results demonstrated that the GMM-based intent estimation method achieves a high accuracy, 91.7% on a straight road and 90.5% on a winding roads, and the intent-estimation- and motion-model-based collision avoidance method improves the safety and comfort of the intelligent vehicle on both straight roads and winding roads. The detection distance of potential collisions, which is crucial in collision avoidance, is increased by more than 8 m. The yaw rate and acceleration during collision avoidance are decreased compared with the traditional method. Our future work will focus on expanding the scope of application of intent estimation, for instance including the moving intent of pedestrians and bicycles. Besides, a further research on the road model will be conducted to increase the accuracy of intent estimation.  
We would like to acknowledge all of our team members, whose contributions were essential for this paper. We would also like to acknowledge the support of National Nature Science Foundations of China (Grant Nos. 61503362, 61305111, 61304100, 91420104), National Nature Science Foundations of Anhui Province (Grant No. 1508085 MF133) and the Guangdong Province Science and Technology Plan (Grant No. 2016B090910002).  
Figure 19: Collision avoidance without intent estimation. (**a**) No collision is detected; (**b**) changing lane to avoid a collision which is represented with a red point; (**c**) avoiding a potential collision successfully. The green box represents the dynamic obstacle detection range of the three-dimensional LIDAR, and the area between the two green lines illustrates the detection area of the IBEO LIDAR. The planned trajectory of the autonomous vehicle is represented with a red line. The velocity and moving direction of detected obstacles are written in red besides the detected obstacles.  
**Author Contributions:** All six authors contributed to this work. [PERSON] was responsible for the literature search, algorithm design and data analysis. [PERSON], [PERSON] and [PERSON] made substantial contributions to the planning and design of the experiments. [PERSON] was responsible for the writing of the paper, and [PERSON] helped modify the paper. Finally, all listed authors approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.