Abstract

Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado Document type: Part of book or chapter of book

Abstract

Recent developments of information technology and mobile lifestyle have forced drivers to multitask while they drive. The in-vehicle “infotainment” technology is already taking its place in the transformation of vehicles towards more intelligent and interactive devices rather than staying as mere transportation convenience. This transformation has several advantages such as easy route navigation, real-time traffic information, and staying connected with work or people while traveling. However, it has several drawbacks concerning the impact on driver cognitive load and attention sources. Therefore, it is crucial to take advantage of state-of-the-art in-vehicle technology to produce counter-measure systems that monitor the driver status and reduce driver workload adaptively depending on the context. In recognition and analysis of the driving context together with driver status monitoring, computer vision applications supply crucial information both in the vehicle (i.e., driver head and eye tracking) and out of the vehicle (i.e., lane, pedestrian, and vehicle detection and tracking, and road sign recognition). In this chapter, we provide a broad range of computer vision applications for CA-IVS from the literature and our previous studies, and we report our current research efforts. Document type: Part of book or chapter of book