Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments  
[PERSON]  
1 Department of Automation, University of Science and Technology of China, Hefei 230026, China; [EMAIL_ADDRESS] (R.H.); [EMAIL_ADDRESS] (X.G. )1  
[PERSON]  
2 Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China; [EMAIL_ADDRESS] (P.Z. ); [EMAIL_ADDRESS] (B.Y. )2  
[PERSON]  
2 Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China; [EMAIL_ADDRESS] (P.Z. ); [EMAIL_ADDRESS] (B.Y. )2  
[PERSON]  
2 Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China; [EMAIL_ADDRESS] (P.Z. ); [EMAIL_ADDRESS] (B.Y. )2  
[PERSON]  
Using traditional solutions, the dynamic obstacles can be detected by fusing information from different sensor in adjacent frames. Then, potential collisions can be predicted and avoided using a motion model that assumes that the obstacles are moving at a constant speed or with a constant acceleration. In Boss [5], which is the winner of the 2007 DARPA Urban Challenge, a dynamic obstacle list is generated by fusing data from different sensors, and moving obstacles are modelled with the point model or the box model. A multi-layer mechanism is used to detect potential collisions, and moving obstacles are assumed to move along lanes [6]. A dynamic obstacle is passed to a more accurate collision checking layer if a collision between the autonomous vehicle and the dynamic obstacle is detected in a rough layer. A proper local path will be generated to avoid the potential collision if the most accurate layer concludes that a collision will occur. In Junior [7], the autonomous vehicle that placed second in the Urban Challenge, dynamic obstacles are detected by comparing two synthetic scans acquired from different sensors over a brief time interval, and particle filters are used to track the obstacles. Then, an obstacle is avoided with a linear model. In 2012, a new dynamic obstacle avoidance and representation method [8] was proposed that takes advantage of data from 3D LIDAR (Light Detection and Ranging) and 2D LIDAR. A collision point is obtained with a linear model that assumes that the dynamic obstacles are moving along the lane at a constant velocity, and the intelligent vehicle avoids the collision point to prevent potential collision. These collision avoidance methods may fail when the detected vehicles are maneuvering, especially on winding roads (in transportation engineering, the term \"horizontal curves\" is used instead of \"winding road\"), which may lead to collisions or undesirable evasive paths. The velocity-based intent-estimation method was introduced in [9; 10] and is used to predict obstacle maneuvers at road intersections. However, this method is limited to intersections, and no accurate trajectory of the detected vehicles is available, which is crucial for collision avoidance. In addition, a driver behavior estimation method that maps the continuous moving states of detected vehicles into a discrete space of maneuvers was proposed by [PERSON] [11]. This method has not been used in the collision avoidance system for autonomous vehicles, and no road information is considered, resulting in a high false detection rate on winding roads. Inspired by this method, we have proposed an intent-estimation-based collision avoidance method for autonomous vehicles [12], and our experimental results show that the performance of the collision avoidance system is improved applying this method. However, the accuracy of the intent estimation is not sufficiently high for urban environments, as a fixed threshold is used to decide the driving intent, and the motion model used to predict the trajectory is rough. To improve safety and comfort, a new intent-estimation- and motion-model-based (IEMMB) collision avoidance method that combines driving intent and a motion model is proposed in this paper.