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The major contribution of this paper is to propose a low-cost accurate distance estimation approach. It can potentially be used in driver modelling, accident avoidance and autonomous driving. Based on MATLAB and Python, sensory data from a Continental radar and a monocular dashcam were fused using a Kalman filter. Both sensors were mounted on a Volkswagen Sharan, performing repeated driving on a same route. The established system consists of three components, radar data processing, camera data processing and data fusion using Kalman filter. For radar data processing, raw radar measurements were directly collected from a data logger and analyzed using a Python program. Valid data were extracted and time stamped for further use. Meanwhile, a Nextbase monocular dashcam was used to record corresponding traffic scenarios. In order to measure headway distance from these videos, object depicting the leading vehicle was first located in each frame.

The major contribution of this paper is the general description of a complete integrating procedure of autonomous vehicle system. Using Robot Operating System (ROS) as the framework, process from senor integration to path planning and path tracking were performed. Based on an off-road All-Terrain Vehicle, an Extended Kalman filter based autonomous control strategy was developed on the ROS. Both the position estimation and autonomous control were performed on the ROS platform. For the position estimation phase, sensory measurements from GPS, IMU and wheel odometry were acquired and processed on ROS. In accordance with the ROS architecture, separate packages were developed for each sensor to gather and publish corresponding measurements. Furthermore, Extended Kalman filtering was performed to fuse all sensory measurements to achieve an optimizing accuracy.