Vehicle collision warning system and collision detection algorithm based on vehicle infrastructure integration

Author(s):  
Yunpeng Wang ◽  
E. Wenjuan ◽  
Daxin Tian ◽  
Guangquan Lu ◽  
Guizhen Yu ◽  
...  
Author(s):  
Jung Kyu Park Et.al

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.


2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.


Author(s):  
Jihua Huang ◽  
Han-Shue Tan

Collision Warning Systems (CWSs) are becoming an important part of vehicle active safety. Commercial CWSs using information measured by the subject vehicle have been available on trucks, buses and passenger cars. Another emerging trend in the development of CWS is based on the cooperative driving concept, where vehicles are equipped with absolute-positioning systems and inter-vehicle communications. The absolute positions, together with the rich information from vehicle motion sensors, facilitate the prediction of vehicle future trajectories. With the current positions and the predicted trajectories of the surrounding vehicles, each vehicle in principle can establish a comprehensive understanding and anticipation of its driving environment. To leverage these potential advantages of the cooperative driving concept, this paper proposes, designs and experiments a collision warning system based on vehicle future-trajectory prediction. A systematic approach with probability measures is designed to detect potential trajectory conflict between the predicted trajectories. Collision detection is then based on the potential trajectory conflicts with examination of their persistency and urgency. Experimental results are provided to verify the feasibility of the design.


Author(s):  
Huiyan Qu ◽  
Wenhui Li ◽  
Wei Zhao

In recent years, with the growth of China’s economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people’s lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide.


2020 ◽  
Vol 4 (4) ◽  
pp. 231
Author(s):  
Agus Mulyanto ◽  
Rohmat Indra Borman ◽  
Purwono Prasetyawan ◽  
A Sumarudin

The advanced driver assistance systems (ADAS) are one of the issues to protecting people from vehicle collision. Collision warning system is a very important part of ADAS to protect people from the dangers of accidents caused by fatigue, drowsiness and other human errors. Multi-sensors has been widely used in ADAS for environment perception such as cameras, radar, and light detection and ranging (LiDAR). We propose the relative orientation and translation between the two sensors are things that must be considered in performing fusion. we discuss the real-time collision warning system using 2D LiDAR and Camera sensors for environment perception and estimate the distance (depth) and angle of obstacles. In this paper, we propose a fusion of two sensors that is camera and 2D LiDAR to get the distance and angle of an obstacle in front of the vehicle that implemented on Nvidia Jetson Nano using Robot Operating System (ROS). Hence, a calibration process between the camera and 2D LiDAR is required which will be presented in session III. After that, the integration and testing will be carried out using static and dynamic scenarios in the relevant environment. For fusion, we use the implementation of the conversion from degree to coordinate. Based on the experiment, we result obtained an average of 0.197 meters


Author(s):  
Tianping Liu ◽  
Yunpeng Wang ◽  
E. Wenjuan ◽  
Daxin Tian ◽  
Guizhen Yu ◽  
...  

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