scholarly journals Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Wenhui Li ◽  
Peixun Liu ◽  
Ying Wang ◽  
Hongyin Ni
Keyword(s):  
Similarity Measure ◽  
Choquet Integral ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Collision Warning ◽  
Feature Similarity ◽  
Driving Assistance ◽  
Forward Collision Warning

Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

A Fast Forward Vehicle Detection Method by Multiple Vision Clues Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 2647-2651
Author(s):  
Tai Qi Wu ◽  
Ye Zhang ◽  
Bin Bin Wang ◽  
Jia Heng Yu ◽  
De Wei Zhu
Keyword(s):  
Detection Method ◽  
System Development ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Vehicle Body ◽  
Detection Accuracy ◽  
Collision Warning ◽  
Moving Vehicle ◽  
Forward Collision Warning

With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

2021 ◽  
Vol 13 (16) ◽  
pp. 8759
Author(s):  
Wei Ye ◽  
Yueru Xu ◽  
Feixiang Zhou ◽  
Xiaomeng Shi ◽  
Zhirui Ye
Keyword(s):  
Mixed Model ◽  
Warning System ◽  
Gaussian Mixture ◽  
Bus Drivers ◽  
Collision Warning ◽  
Lane Departure ◽  
Driving Assistance ◽  
Naturalistic Driving ◽  
Forward Collision Warning ◽  
Driving Assistance System

Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

Realtime On-Road Vehicle Detection Approach Based on Cascaded Structure

2011 ◽  
Vol 130-134 ◽  
pp. 2429-2432
Author(s):  
Liang Xiu Zhang ◽  
Xu Yun Qiu ◽  
Zhu Lin Zhang ◽  
Yu Lin Wang
Keyword(s):  
Warning System ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Assistance ◽  
Road Vehicle ◽  
Collision Warning ◽  
Detection Approach ◽  
Transportation Applications

Realtime on-road vehicle detection is a key technology in many transportation applications, such as driver assistance, autonomous driving and active safety. A vehicle detection algorithm based on cascaded structure is introduced. Haar-like features are used to built model in this application, and GAB algorithm is chosen to train the strong classifiers. Then, the real-time on-road vehicle classifier based on cascaded structure is constructed by combining the strong classifiers. Experimental results show that the cascaded classifier is excellent in both detection accuracy and computational efficiency, which ensures its application to collision warning system.

scholarly journals UAV Collision Detection Algorithm based on Formulafor Fast Calculation

2021 ◽  
Vol 12 (6) ◽  
pp. 452-456
Author(s):  
Jung Kyu Park Et.al
Keyword(s):  
Collision Detection ◽  
Movement Time ◽  
Warning System ◽  
Detection Algorithm ◽  
Vehicle Safety ◽  
Collision System ◽  
Fast Calculation ◽  
Collision Warning ◽  
Collision Avoidance System ◽  
Forward Collision Warning

Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.

Real-Time Side-Rear Vehicle Detection Algorithm for Blind Spot Warning Systems

2017 ◽  
Vol 23 (7) ◽  
pp. 408-416
Author(s):  
Hyunwoo Kang ◽  
Jang Woon Baek ◽  
Byung-Gil Han ◽  
Yoonsu Chung
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Detection Algorithm ◽  
Warning Systems

scholarly journals Net-societal and Net-private Benefits of Some Existing Vehicle Crash Avoidance Technologies

2019 ◽  
Author(s):  
Abdullah Khan ◽  
Corey D. Harper ◽  
Chris T. Hendrickson ◽  
Constantine Samaras
Keyword(s):  
Human Error ◽  
Blind Spot ◽  
Warning Systems ◽  
Private Benefits ◽  
Collision Warning ◽  
Societal Benefits ◽  
Light Duty ◽  
Crash Avoidance ◽  
Light Duty Vehicle ◽  
Forward Collision Warning

Most light-duty vehicle crashes occur due to human error. Many of these crashes could be avoided or made less severe with the aid of crash avoidance technologies. These technologies can assist the driver in maintaining control of the vehicle when a possibly dangerous situation arises by issuing alerts to the driver and in a few cases, responding to the situation itself. This paper estimates the societal and private benefits and costs associated with three crash avoidance technologies, blind-spot monitoring, lane departure warning, and forward-collision warning, for all light duty passenger vehicles in the U.S. for the year 2015. The three technologies could collectively prevent up to 1.6 million crashes each year including 7,200 fatal crashes. In this paper, the authors estimated the net-societal benefits to the overall society from avoiding the cost of the crashes while also estimating the private share of those benefits that are directly affecting the crash victims. For the first generation warning systems, net-societal benefits and net-private benefits are positive. Moreover, the newer generation of improved warning systems and active braking should make net benefits even more advantageous.

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