The major contribution of this paper is to propose a low-cost accurate distance estimation approach. It is the first step of a research program aiming to model driving style variance. This paper proposes to fuse long range radar and monocular camera using Kalman filter, and 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 Kalman filter. Both sensors were mounted on a VW 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 recorded videos, vanishing points were first detected in each frame. Afterwards, object depicting leading vehicle was located. The headway distance can hence be obtained by assuming leading and host vehicles were in the same ground plane. After both sensory data were obtained, they were synthesized and fused using Kalman filter, to generate a better estimation of headway distance. The performances of both sensors were assessed individually. Then the results were compared with Kalman filter to investigate the optimization performance of the data fusion approach. This is a general guidance of headway distance estimation using low cost radar and monocular camera. With the described general procedures, this paper can allow researchers to easily process and fuse radar and camera measurements to obtain optimized headway distance estimation.