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SUN ET AL.: ON-ROAD VEHICLE DETECTION: A REVIEW

Fig. 1. The variety of vehicle appearances poses a big challenge for vehicle detection.

computation-intensive video-processing algorithms even on a low-end PC in realtime.

With the ultimate goal of building autonomous vehicles, many government institutions, automotive manufacturers and suppliers, and R&D companies have launched various projects worldwide, involving a large number of research units working cooperatively. These efforts have produced several prototypes and solutions, based on rather different approaches [5], [12], [13], [6]. Looking at research on intelligent vehicles worldwide, Europe pioneers the re- search, followed by Japan and United States.

In Europe, the PROMETHEUS project (Program for European Traffic with Highest Efficiency and Unprecedented Safety) started this exploration in 1986. More than 13 vehicle manufactures and research institutes from 19 European countries were involved. Several prototype vehicles and systems were designed and demonstrated as a result of PROMETHEUS. In 1987, the UBM (Universitaet der Bundes- wehr Munich) test vehicle VaMoRs demonstrated the capability of fully autonomous longitudinal and lateral vehicle guidance by computer vision on a 20 km free section of highway at speed up to 96 km/h. Vision was used to provide input for both lateral and longitudinal control. That was considered as the first milestone.

Further development of this work has been in collabora- tion with von Seelen’s group [14] and Daimler-Benz VITA project (VIsion Technology Application) [15]. Long range autonomous driving has been demonstrated by the VaMP of UBM in 1995. The trip was from Munich to Odense, Denmark, more than 1,600 km. About 95 percent of the distance was driven without intervention of the safety driver [3]. Another experimental vehicle, mobile laboratory (MOB-LAB), was also part of the PROMETHEUS project [16]. It was equipped with four cameras, several computers, monitors, and a control-panel to give a visual feedback and warnings to the driver. One of the most promising subsystems in the MOB- LAB was the Generic Obstacle and Lane Detection (GOLD) system. The GOLD system, utilizing a stereo rig in the MOB- LAB, addressed both lane and obstacle detection at the same

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time. The lane detection was based on a pattern matching technique, while the obstacle detection was reduced to the determination of the free-space in front of the vehicle using the stereo image pairs without 3D reconstruction. The GOLD system has been ported on ARGO, a Lancia Thema passenger car with automatic steering capabilities [17].

Although the first research efforts on developing intelli- gent vehicles were seen in Japan in the 1970s, significant research activities have been triggered by prototype vehicles built in Europe in the late-1980s and early-1990s. MITI, Nissan, and Fujitsu pioneered the research in this area by joining forces in the project “Personal Vehicle System” [18], a project with deep influence on Japan. In 1996, the Advanced Cruise-Assist Highway System Research Association (AHSRA) was established among automobile industries and a large number of research centers in Japan [5]. The Japanese Smartway concept car will implement some driver aid features, such as lane keeping, intersection collision avoid- ance, and pedestrian detection. A model deployment project was planned to be operational by 2003 and national deployment in 2015 [6].

In the United States, a number of initiatives have been launched to address this problem. In 1995, the US govern- ment established the National Automated Highway System Consortium (NAHSC) [19], and launched the Intelligent Vehicle Initiative (IVI) in 1997. Several promising prototype vehicles/ systems have been investigated and demonstrated within the last 15 years [20]. The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The group has produced a series of 11 vehicles, Navlab 1 through Navlab 11. Their applications have included off-road scout- ing, automated highways, run-off-road collision prevention, and driver assistance for maneuvering in crowded city environments. In 1995, NavLab5 demonstrated long range partially autonomous driving (i.e., automatic lateral control) on highways from the east coast to the west. With a more than 5,000 km trip, 98 percent of the distance was driven without intervention of the human safety driver [21]. The latest model in the Navlab family is the Navlab 11, a robot Jeep Wrangler equipped with a wide variety of sensors for short-range and midrange obstacle detection [22], [23], [20].

Major motor companies including Ford and GM have poured great effort into this research and already demon- strated several promising concept vehicles. US government agencies are very supportive of intelligent vehicle research. Recently, the US Department of Transportation (USDOT) has launched a five year, 35 million dollar project with GM to develop and test preproduction rear-end collision avoidance system [6]. In March 2004, the whole world was stimulated by the “grand challenge” organized by The US Defense Advanced Research Projects Agency (DARPA) [24]. In this competition, 15 fully autonomous vehicles attempted to independently navigate a 250-mile (400 km) desert course within a fixed time period, all with no human intervention whatsoever—no driver, no remote-control, just pure computer-processing and navigation horsepower, competing for a 1 million cash prize. Although, even the best vehicle (i.e., “Red Team” from Carnegie Mellon) made only seven miles, it was a very big step towards building autonomous vehicles in the future.

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