This paper proposes a map matching-based driving lane recognition system for lowcost precise vehicle positioning on highways. The proposed method finds the position where the road boundaries detected by a LIDAR sensor are best matched with the road boundaries of the precise digital map and then recognizes the driving lane based on the position. To improve the limitations of the existing Chamfer matching method, this paper proposes the following two methods. The first is a method of generating one candidate position for each lane by using the lane information of the map and the left and right lane offsets obtained from the front camera. Second is a simple matching method that converts the LIDAR and map data into arrays and compares only the lateral distances to measure the matching score. In addition, this paper proposes a method for maintaining sufficient road boundary information in every frame by applying a temporal accumulation to the LIDAR data. In the experiment, the proposed method was quantitatively evaluated using a database acquired from sensors mounted on a real vehicle on highways. The experimental results show that the driving lane recognition rate is 99.56%, the lateral position error is 0.49m, and the processing time is 6.4ms. These results prove that the proposed system provides sufficient accuracy and reliability even in the presence of GPS position errors in various highway environments.

An image signal processor (ISP) is a dedicated processor that transforms raw data obtained from camera sensors into an image that satisfies the requirements of a specific application or use case. An ISP typically has many tuning parameters due to the complexity of the image transformation. Until now, these are generally tuned by human experts manually, and this work takes a great deal of time. This paper proposes an application-level automatic ISP parameter tuning system. In particular, this paper focuses on a rear view monitoring (RVM) camera that is mounted on the rear side of the vehicle to prevent backovers in advanced driving assistance systems (ADAS). The proposed system consists of four steps. The first step is the input image generation, which captures a virtual scene including the test site and vehicle body whose three-dimensional (3D) models are created according to the test requirements and vehicle design. The second step is ISP processing, which transforms the input image into an ISP output image (RVM image) according to the ISP specification in the RVM system. The third step is to evaluate the RVM image's fitness using evaluation criteria (EC) functions. Finally, ISP parameters are tuned by the sequential-torandom search method. In the experiment, the proposed system is evaluated by using the 3D modeling data of six different test vehicle types. Experimental results show that the proposed system can effectively obtain the usable ISP parameters' values that satisfy all RVM requirements for a given situation, regardless of test vehicle type, within 2~3 hours.INDEX TERMS Automatic parameter tuning, grid-to-random search, image signal processor (ISP), rear view monitoring camera.