Recursive 3-D road and relative ego-state recognition

Dickmanns, Ernst D.; Mysliwetz, Birger D.

doi:10.1109/34.121789

Cited by 490 publications

(243 citation statements)

References 17 publications

Supporting

Mentioning

232

Contrasting

Unclassified

Order By: Relevance

“…Aspects of the lane-finding problem have been studied for decades in the context of autonomous land vehicle development (Dickmanns and Mysliwetz 1992;Thorpe et al 1988) and driver-assistance technologies (Bertozzi and Broggi 1998;Bertozzi et al 2000;Apostoloff and Zelinsky 2004;Fletcher and Zelinsky 2006;Kim 2008). McCall and Trivedi provide an excellent survey (McCall and Trivedi 2006).…”

Section: Related Workmentioning

confidence: 99%

Finding multiple lanes in urban road networks with vision and lidar

et al. 2009

View full text Add to dashboard Cite

This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior.Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road.We analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closedloop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km/h amidst moving traffic.

show abstract

Section: Related Workmentioning

confidence: 99%

Finding multiple lanes in urban road networks with vision and lidar

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Robot driving has concentrated on forward-looking sensing, for road following and obstacle detection [21,22,23,24,25,26,27]. This is an appropriate first step, but real deployment of mobile robots will require additional sensing and reasoning to surround the robot with safeguard sensors and systems.…”

Section: The Need For 360 Degree Safeguardingmentioning

confidence: 99%

Perception for collision avoidance and autonomous driving

et al. 2003

View full text Add to dashboard Cite

The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads. Robotics Technology and Intelligent VehiclesDuring the decade of the 1990's, researchers from automotive engineering, from robotics, and from allied fields came together to start building intelligent vehicles [1,2,3,4]. These vehicles use sensors, actuators, processors, and communications systems to either drive automatically, or to monitor a human driver and assist in navigation or warn in case of a developing dangerous situation.The field was driven by several market demand factors. First is an increasing interest in safety. While the accident rate has decreased steadily for 40 years, the total miles traveled have increased, with the net result that the number of road fatalities in the US has stayed approximately constant, averaging 40,000 / year [5]. Furthermore, there is an increasing sense that vehicles have become safer (air bags, anti-lock brakes, crumple zones); roadways have become safer (the Interstate highway system); societal pressures have become stronger (mandatory child safety seats, serious enforcement of drunk driving laws) but the drivers themselves have not necessarily improved. Currently, in the US, 70% of all crashes are primarily caused by the drivers, and in another 20% driver error is a major contributing factor [5,6].Beyond safety, the next major driving factors have to do with congestion and pollution. In the US, traffic continues to grow at 4% / year, while with the completion of the Interstate highway system, major new road construction greatly lags traffic growth. Computercontrolled vehicles promise to smooth out traffic flow, permitting more vehicles to travel in

show abstract

“…A comparison of lane position detection and tracking techniques is presented in [4]. One of the methods listed is described in detail in [5]. This approach uses a road model based on clothoids and tracks them by a linear vehicle dynamics model.…”

Section: Introductionmentioning

confidence: 99%

Stable Road Lane Model Based on Clothoids

Gackstatter

Heinemann

Thomas

et al. 2010

Advanced Microsystems for Automotive Applications 2010

View full text Add to dashboard Cite

In the following, the main concepts of a road lane model are presented that keeps track of an arbitrary number of lane borders. Information from an existing lane detection device, a gyrosensor, and map data are merged and filtered to create a road model with a desired number of road lines. The model is based on clothoids and continuously provides positions, angles, and curvatures of the border lines of the vehicle's own lane as well as of several neighboring lanes. Particularly on urban roads, in situations with upcoming turning lanes, or when the lane detection system fails to detect road lines, the model can still provide plausible information. This information significantly simplifies the situation analysis in further algorithms that rely on a lane detection system and require detailed information on current road lanes.

show abstract

Recursive 3-D road and relative ego-state recognition

Cited by 490 publications

References 17 publications

Finding multiple lanes in urban road networks with vision and lidar

Finding multiple lanes in urban road networks with vision and lidar

Perception for collision avoidance and autonomous driving

Stable Road Lane Model Based on Clothoids

Contact Info

Product

Resources

About