A vision-based off-road alert system

Brattoli, M.; Tasca, R.; Tomasini, A.; Chioffi, E.; Gerna, D.; Pasotti, M.

doi:10.1109/ivs.1996.566377

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Most of the research shares and combines various principles from these methods. Measuring the relative position between lane boundaries and a vehicle has been a well-known method for the design of LDI because of its simple concept [3,4,12,13,15]. This method relies on the localization of lane marks to obtain the offset between the center of the lane and the central axis of a car body and often needs camera calibration and both widths of the vehicle and the lane.…”

Section: Introductionmentioning

confidence: 99%

A lane-departure identification based on LBPE, Hough transform, and linear regression

Lee

Yi²

2005

Computer Vision and Image Understanding

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Section: Introductionmentioning

confidence: 99%

A lane-departure identification based on LBPE, Hough transform, and linear regression

Lee

Yi²

2005

Computer Vision and Image Understanding

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“…Researchers have developed lots of approaches for this task depending on different types of sensors employed [1][2]. To operate efficiently and safely, a perceptual system should provide lots of information for the off-road vehicle: possible roads, obstacles (stationary or moving), ditches/caves, risky terrains, etc.…”

Section: Introductioinmentioning

confidence: 99%

An Environmental Perception System to Autonomous Off-Road Navigation by using Multi-sensor Data Fusion

Xiang

2005 International Conference on Neural Networks and Brain

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Environmental perception is one of the most difficult problems on the research of off-road autonomous vehicles. This paper describes a multisensor data fusion based environmental perception system for off-road autonomous navigation. The system is composed of one camera, four laser range finders, one microwave radar, and several ultrasonic sensors. A hierarchical structure is used to organize the sensors from feature level to high fusion level. A 2D fusion map consisting six classifications is proposed to describe the surrounding environment. Dempster-Shafer evidence theory is adopted to decide the classification of each grid in the fusion map. A weighted evidence combination rule is proposed and implemented to improve the decision results under high conflicting circumstance. The experimental results show that the proposed system is reliable and is able to fulfil the requirements from off-road navigation.

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“…Researchers have developed lots of approaches for this task depending on different types of sensors employed, i.e., mono-vision [2], stereo-vision [1], LADAR [4], radar [3] and ultrasonic sensors [5]. To operate efficiently and safely, a perceptual system should provide lots of information for the off-road vehicle: possible roads, obstacles (stationary or moving), ditches/caves, risky terrains, etc.…”

Section: Introductioinmentioning

confidence: 99%

Environmental perception and multi-sensor data fusion for off-road autonomous vehicles

Xiang

Özgüner

Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.

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Environmental perception is one of the most difficult problems for off-road autonomous vehicles. Due to the variety and complexity of off-road environments, the information from any single sensor is not enough for safe and efficient vehicle navigation. Employing more sensors can greatly improve the vehicle's perceptive capability. This paper describes a multi-sensor data fusion system for off-road autonomous vehicles. The system acquires data from one camera, four laser range finders, one radar, and several ultrasonic sensors. A hierarchical structure is used to organize the sensors from feature level to high fusion level. Dempster-Shafer evidence theory is adopted to decide the classification of each grid in the fusion map. A weighted evidence combination rule is proposed and implemented to improve the decision results under high conflicting circumstance. The experimental results showed the validity of our method.

show abstract

A vision-based off-road alert system

Cited by 8 publications

References 6 publications

A lane-departure identification based on LBPE, Hough transform, and linear regression

A lane-departure identification based on LBPE, Hough transform, and linear regression

An Environmental Perception System to Autonomous Off-Road Navigation by using Multi-sensor Data Fusion

Environmental perception and multi-sensor data fusion for off-road autonomous vehicles

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