Vision-based long-distance lane perception and front vehicle location for full autonomous vehicles on highway roads

A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads. Firstly, a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways. By using a novel robust lane marking feature which combines the constraints of intensity, edge and width, the lane markings in far regions were extracted accurately and efficiently. Next, the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter. Finally, front vehicles were located on correct lanes using the tracked lane lines. Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%. The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions. This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.