Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System

Song, Wenjie; Yang, Yi; Fu, Mengyin; Fan, Qunbo; Wang, Meiling

doi:10.1109/tits.2017.2700628

Cited by 97 publications

(30 citation statements)

References 42 publications

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“…(1) The driver should control the speed. It was seen from the experimental results of this study that the speed was the most direct and key factor affecting the probability of rear-end collision [13]. The simulation experiment also verified that there was still a deviation in the accuracy rate and timeliness rate in high-speed driving even when the pre-warning system was used for preventing rear-end collision.…”

Section: Discussionsupporting

confidence: 59%

Analysis of Rear-End Collision Accident of Urban Traffic Based on Safety Pre-Warning Algorithm

Wang¹,

Li²,

Kong³

2020

Preprint

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With the increase of per capita car ownership, traffic accidents frequently occur, in which rear-end collision accounts for 30% to 40% of the total accidents; thus, rear-end collision has become the primary factor of traffic environment deterioration. Therefore, how to improve road traffic safety and reduce the probability of rear-end collision has become a major social concern. In this study, based on the safety pre-warning algorithm, a vehicle collision model was built, and a vehicle anti-collision warning system was established. The calculation was performed based on the sample data to obtain the prediction value of vehicle collision time under different driving speeds, so as to provide drivers with effective response time and reduce the casualties and property losses caused by a vehicle collision. The experimental results showed that the accuracy rate of the pre-warning reached 80% when the speed was regarded as a variable, and the simulation results showed that the early pre-warning or delayed pre-warning rate was very low, and the timeliness rate reached 89%, which enables drivers to react quickly in the appropriate time and effectively reduces the risk of vehicle rear-end collision.

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Section: Discussionsupporting

confidence: 59%

Analysis of Rear-End Collision Accident of Urban Traffic Based on Safety Pre-Warning Algorithm

Wang¹,

Li²,

Kong³

2020

Preprint

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“…The partial occlusion of an object by a structure in the image is difficult to detect, since it is not always possible to differentiate between the object changing shape and the becoming occluded. A common approach to handling complete occlusion during tracking is to model the object's motion by either linear or nonlinear models and, in the case of occlusion, to continue predicting the object's location until it reappears [11,20]. The chance of occlusion can be reduced by means of an appropriate selection of camera positions.…”

Section: Discussionmentioning

confidence: 99%

“…With respect to the mapping ROI models: the problem of trajectory prediction has areas of opportunity such as detection failures, objects with similar appearances, occlusions, and variations in illumination and points of view [11,20]. Only two kinds of obstacles (vehicles and pedestrians) are taken into consideration [3,11,17,20,19]. High processing runtime rate and low obstacle detection rate [3,14].…”

Section: Analysis Of Related Workmentioning

confidence: 99%

Obstacle Detection and Trajectory Estimation in Vehicular Displacements based on Computational Vision

Reyes-Cocoletzi¹,

Olmos-Pineda²,

Olvera-López³

2019

RCS

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Obstacle detection and trajectory estimation in vehicular environments is an open problem in autonomous vehicles development. The automotive industry has made significant progress in research and development of tools; however, there are still challenges to overcome and opportunity areas to be exploited in order to achieve full autonomy in vehicles. This paper presents an analysis of different methods proposed for obstacle detection and trajectory estimation, leading into a proposal of solution for solving the problem of trajectory estimation based on computer vision techniques. This proposal covers the context of traffic environments in Latin America, where basic signage (such as the dividing lines of the road) is absent or not easily visible, among other typical characteristics of countries (like Mexico and others) where the infrastructure for maintaining safe road conditions (speedbumps, potholes) is limited.

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“…As the environmental factors have not been effectively quantified, the causal relationship between environmental transition and driving behavior has been insufficiently determined; that is, very few previous studies have applied specific indicators to show how environmental transition can influence driving behavior and visual perception. Furthermore, we expect to extract out quantified environmental factors from driving video by image recognition technology [31,32], and then, we may take quantified environmental factors into analysis to deepen our understanding of the traffic safety problem of tunnel entrance.…”

Section: Limitationmentioning

confidence: 99%

Variations in Naturalistic Driving Behavior and Visual Perception at the Entrances of Short, Medium, and Long Tunnels

Shi-jian

et al. 2020

Journal of Advanced Transportation

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Driver behavior and visual perception are very important factors in the management of traffic accident risk at tunnel entrances. This study was undertaken to analyze the differences in driving behavior and visual perception at the entrances of three types of tunnels, namely, short, medium-length, and long tunnels, under naturalistic driving conditions. Using three driving behavior indicators (speed, deceleration, and position) and two visual perception indicators (fixation and saccade), the driving performance of twenty drivers at six tunnels (two tunnels per condition) was comparatively analyzed. The results revealed that the speed maintained by the drivers prior to deceleration with braking under the short-tunnel condition was significantly larger than that under the medium- and long-tunnel conditions and that the drivers had a greater average and maximum deceleration rates under the short-tunnel condition. A similar general variation of driver visual perception appeared under the respective tunnel conditions, with the number of fixations gradually increasing and the maximum saccade amplitude gradually decreasing as the drivers approached the tunnel portal. However, the variation occurred approximately 60 m earlier under the short-tunnel condition than under the medium- and long-tunnel conditions. Interactive correlations between driving behavior and visual perception under the three conditions were established. The commencement of active deceleration was significantly associated (with correlation factors of 0.80, 0.77, and 0.79 under short-, medium-, and long-tunnel conditions, respectively) with the point at which the driver saccade amplitude fell below 10 degrees for more than 3 s. The results of this study add to the sum of knowledge of differential driver performance at the entrances of tunnels of different lengths.

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Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System

Cited by 97 publications

References 42 publications

Analysis of Rear-End Collision Accident of Urban Traffic Based on Safety Pre-Warning Algorithm

Analysis of Rear-End Collision Accident of Urban Traffic Based on Safety Pre-Warning Algorithm

Obstacle Detection and Trajectory Estimation in Vehicular Displacements based on Computational Vision

Variations in Naturalistic Driving Behavior and Visual Perception at the Entrances of Short, Medium, and Long Tunnels

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