VisLab and the Evolution of Vision-Based UGVs

The technological issues and legal considerations associated with fully automated vehicles have prompted the automotive industry to focus more on supervised systems and advanced driver assistance systems (ADAS). Transportation departments worldwide are concerned with social, economic, or environmental objectives aimed at enhancing fuel and road network efficiency and quality of life. Recently, the automotive industry's success with ADAS has induced the military to reconsider its ground-fleet-automation goal. Researchers are considering unmanned-vehicle technology for many other applications. However, most common and attracting the most industry interest is the automation of road vehicles. Unmanned ground vehicles shape our future by providing enhanced safety and improved mobility

[1]  Alberto Broggi,et al.  The TerraMax autonomous vehicle: Field Reports , 2006 .

[2]  Alberto Broggi,et al.  A decision network based frame-work for visual off-road path detection problem , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[3]  Massimo Bertozzi,et al.  Artificial vision in road vehicles , 2002, Proc. IEEE.

[4]  Takeo Kanade,et al.  Vision and Navigation for the Carnegie-Mellon Navlab , 1987 .

[5]  Volker Graefe,et al.  Vision-based autonomous road vehicles , 1992 .

[6]  C. Vision-based Vehicle Guidance , 1992, Springer Series in Perception Engineering.

[7]  Alberto Broggi,et al.  The TerraMax autonomous vehicle , 2006, J. Field Robotics.

[8]  Alberto Broggi,et al.  The Single Frame Stereo Vision System for Reliable Obstacle Detection Used during the 2005 DARPA Grand Challenge on TerraMax , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[9]  Charles E. Thorpe,et al.  Vision and Navigation , 1990 .

[10]  E. D. Dickmanns,et al.  The development of machine vision for road vehicles in the last decade , 2002, Intelligent Vehicle Symposium, 2002. IEEE.