Is it safe to change the lane? — Visual exploration of adjacent lanes for autonomous driving

Lane changes on multi-lane roads are an important and complex task for autonomous driving because the system has to be sure that the adjacent lane is not occupied by any other object. Existing radar-based systems can be complemented by vision-based methods to increase their reliability. This work presents new methods based on multiple pattern recognition strategies, such as image categorization, applied to serially-produced, side-mirror mounted fish-eye cameras. The focus is on appearance-based methods, such as tire detection and structure analysis, and motion-based methods, such as optical flow. Extensive experiments evaluate all presented methods on long video sequences on German highways. The proposed approach is shown to be effective for all kinds of vehicles, all relevant situations, and under varying weather conditions.