A precrash system based on radar for automotive applications

Skutek, Michael; Mekhaiel, Moheb; Wanielik, Gerd

doi:10.1109/ivs.2003.1212879

Cited by 25 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many approaches to assist drivers in avoiding collisions have been developed based on different technologies such as RADAR [2], LASER scanner [3], ultrasounds [4] or vision systems [5][6][7]. As for drawbacks, the vision field of RADAR can be narrow, and mutual interference may occur when other vehicles on road are installed with same equipment due to radar's active sensing characteristic.…”

Section: Related Workmentioning

confidence: 99%

Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic

Liu

Wang

et al. 2015

Intelligent Computing Theories and Methodologies

View full text Add to dashboard Cite

Abstract. Extensive research has been carried out in the field of driver assistance systems in order to increase road safety and comfort. We propose a pose estimation algorithm based on binocular stereo vision for calculating the pose of on-road vehicles and providing reference for the decision of driving assistant system, which is useful for behavior prediction for vehicles and collision avoidance. Our algorithm is divided into three major stages. In the first part, the vehicle is detected and roughly located on the disparity map. In the second part, feature points on the vehicle are extracted by means of license plate detection algorithm. Finally, pose information including distance, direction and its variation is estimated. Experimental results prove the feasibility of the algorithm in complex traffic scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic

Liu

Wang

et al. 2015

Intelligent Computing Theories and Methodologies

View full text Add to dashboard Cite

show abstract

“…Many approaches to assist drivers in avoiding collisions have been developed based on different technologies such as RADAR [3], LASER scanner [4], ultrasounds [5], or vision systems [6][7][8].…”

Section: Related Workmentioning

confidence: 99%

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Arenado

Oria

Torre-Ferrero

et al. 2014

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Abstract-This paper presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an Intelligent Transportation System (ITS). The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection we use a combination of two features: the shadow underneath the vehicle and horizontal edges. We propose a new method for shadow thresholding based on the grayscale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardized in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

show abstract

“…They use such sensors as: a telemeter like radar [3], a laser scanner [4], cooperative detection systems [5], or monocular vision for detecting specific features like edges, symmetry [6], colour [7], or movement [8]. Most monocular approaches are capable of recognizing vehicles and pedestrians.…”

Section: Introductionmentioning

confidence: 99%

The ArosDyn project: Robust analysis of dynamic scenes

Paromtchik

Laugier

Perrollaz

et al. 2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

View full text Add to dashboard Cite

The ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations.

show abstract

A precrash system based on radar for automotive applications

Cited by 25 publications

References 0 publications

Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic

Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

The ArosDyn project: Robust analysis of dynamic scenes

Contact Info

Product

Resources

About