Vision‐based inter‐vehicle distance estimation for driver alarm system

Liu, Zewei; Lu, Dongming; Qian, Wei; Ren, Kan; Zhang, Jun; Li-wei, XU

doi:10.1049/iet-its.2018.5313

Cited by 12 publications

(3 citation statements)

References 11 publications

(15 reference statements)

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“…An Infrared (IR) emitted quantum cascade laser is used to illuminate these adverse environmental conditions properly. A vision-based traffic accident detection method was proposed to investigate the imbalance between normal traffic and traffic accident [12], [24]. A machine learning algorithm was proposed to recognize the road surfaces [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Development of TRINETRA : A Sensor Based Vision Enhancement System for Obstacle Detection on Railway Tracks

Saket¹

2021

Preprint

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The authors have developed a prototype, which promises easy passage for trains in unknown environments such as zero visibility situations, fog, smog, and heavy rain. The prototype is based upon the integration of a camera, Radio Detection and Ranging (RADAR), and Infrared (IR) Light Amplification by Stimulated Emission of Radiation (LASER).The camera used in the prototype catches a long-range view of the track and presents it live on a mini screen fixed in the loco pilot cabin. A combined short, mid, and long-range radar sensor system is used to detect obstacles continuously in loco pilot blind spots on the track, particularly for collision avoidance assistance at high speed. The present work proposes a long-range laser IR illuminator with a wide range of color and mono cameras to aid clear and precise monitoring in zero visibility conditions, which is fixed on the front portion/engine of the locomotive/engine. The prototype experimental results for 2 m - 2 km distances have been performed on a live running train, which shows that the developed prototype tracks obstacles effectively during fog and smog conditions. The design concept, observation, prototype model, and other technical specifications have been presented, and satisfactory results were found.

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Section: Introductionmentioning

confidence: 99%

Development of TRINETRA : A Sensor Based Vision Enhancement System for Obstacle Detection on Railway Tracks

Saket¹

2021

Preprint

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“…Cameras are useful for object recognition, but there are times when measuring distances robustly is difficult; recent sensor fusion studies attempting robust distance measurements have been undertaken [1,2]. Methods utilizing cameras detect a vehicle from an image, and the distance is estimated by defining a proportional expression between the image coordinate system and the actual distance [3][4][5]. A recent study made use of deep learning to estimate positions; however, this method produced errors when a vehicle passed by on a slope [6].…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Kim

Park

2020

Sensors

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Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

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“…The existing passive vision-based methods used in V-DAS can be further divided into two subcategories [8]: stereo vision-based approaches and monocular vision-based approaches. The former uses multiple view geometry and stereo image pairs to rebuild a 3D space and generate the depth information of the target.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

et al. 2020

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As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth values as inputs to train a deep learning network with an encoding–decoding structure. Compared with unsupervised networks without sparse depth labels, the proposed semi-supervised network improves the estimation accuracy of depth maps. Secondly, this paper combines the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle or pedestrian. Specifically, for measuring the distance between the subject vehicle and a pedestrian, this paper proposes a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of this pedestrian. To measure the distance between the subject vehicle and the target vehicle, this paper proposes a method that first fits a 3-D plane based on the locations of target points in the camera body coordinate using RANSAC (RANdom SAmple Consensus), it then projects all the pixels of the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed method can effectively estimate depth maps. The experimental results in real road scenarios and the KITTI dataset confirm the accuracy of the proposed distance measurement methods.

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Vision‐based inter‐vehicle distance estimation for driver alarm system

Cited by 12 publications

References 11 publications

Development of TRINETRA : A Sensor Based Vision Enhancement System for Obstacle Detection on Railway Tracks

Development of TRINETRA : A Sensor Based Vision Enhancement System for Obstacle Detection on Railway Tracks

Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

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