Vision-based adaptive cruise control using pattern matching

Adaptive Cruise Control (ACC) is a relatively new system designed to assist automobile drivers in maintaining a safe following distance. This paper proposes and validates a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. Pattern matching, with the aid of lane detection, is used for vehicle detection. The vehicle and range detection algorithms are validated using real-world data, and then the resulting system performance is shown to be sufficient using a simulation of a basic vehicle model

A three-step vehicle detection framework for range estimation using a single camera

Abstract: This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows