Illegal Vehicle Research Articles

견인차-피견인차-주행차량의 3중 추돌에 관한 시뮬레이션 해석

The wrecker truck of lift type is used to move damaged car in accident spot or to move illegal vehicle in towaway zone toward pound.. This special vehicle drives by lifting front or rear side of car with the driving type of towed car. Each car is modelled with CATIA and is simulated with FEM analysis program ANSYS. This study analyzes how dangerous is the triple collision among the wrecker-towed car-driving car as comparing with the usual collision accident with cars. It is studied how responsible is the towed car in case of crashing its back side by driving car. The influence on the driving angle of towed car by lifting wrecker is also considered.

Crossing road monitoring system based on adaptive decision for illegal situation

Automatically detecting and tracking crossing lane line vehicles in the illegal situation is an important part of E-police system. However, till now, there is few research works reported for it. In this paper, a novel crossing road monitoring system containing two cameras is built, which is composed of several steps. The long-range camera is used for lane lines extraction by improved multiple Hough transforms, and illegal vehicle is detected according to the distance between vehicle center and lane lines. Then, the illegal vehicle is tracked and license plate is captured by the close-range camera. The records used for police checking contain the video of illegal process and license plate. The proposed system has been used by the traffic administration bureau. The results on actual application show that this novel system can be used for E-police with high detection accuracy.

An illegal lane change monitoring system

In order to solve problems such as traffic jams and poor traffic management, a kind of illegal lane changing monitoring system based on traffic video is presented. The hardware system is composed of industrial computers and cameras fixed above the road. The software part consists of video processing, background extraction and updating, lane line extraction, vehicle recognition, moving shadow analysis, illegal lane change detection, vehicle tracking and prediction. First, the video collected is transformed into a series of images. Then a background is obtained by using averaging, and the lane lines are extracted by an improved Hough transform. In order to obtain complete and accurate outlines of the moving vehicles, a method of obtaining a vehicle profile combined with background subtraction and edge detection is proposed. A simple method for illegal lane change discrimination is also put forward. A mean-shift algorithm combined with a Kalman filter is used to track and predict the locations of vehicles. Experimental results show that this system satisfies the need for real-time accuracy, and it can be used for intelligent traffic supervison.

An intelligent traffic management expert system with RFID technology

This paper presents an intelligent traffic management expert system with RFID technology. The system provides both practically important traffic data collection and control information and can trace criminal or illegal vehicles such as stolen cars or vehicles that evade tickets, tolls or vehicle taxes. The system architecture consists of an RFID reader, a passive tag, a personal computer, a pair of infrared sensors, and a high-speed server with a database system. Based on RFID technology, the system collects and calculates average speed and average flow information on each road of a district area in a city. It then transmits the messages from all the congested roads in a district area to the server in the district center via a communication program. Through a flooding algorithm, each server in a district center exchanges and updates information with all neighbor servers in other district centers so all that the servers in various district centers can get all the latest congestion messages in a city. Therefore, a dynamic navigation system can find the shortest path that avoids congested roads. Meanwhile, we compare three types of tags for choosing a better solution for e-plates in the future. We also adopt infrared sensors for detecting cars that do not have a tag.