Vehicle positioning is critical for inter-vehicle communication, navigation, vehicle monitoring and tracking. They are regarded as the core technology ensuring safety in everyday-driving. This paper proposes an enhanced vehicle ego-localization method based on streetscape image database. It is most useful in the global positioning system(GPS) blind area. Firstly, a database is built by collecting streetscape images, extracting dominant color feature and detecting speeded up robust feature(SURF) points. Secondly, an image that the vehicle shoots at one point is analyzed to find a matching image in the database by dynamic programming(DP)matching. According to the image similarity, several images with higher probabilities are selected to realize coarse positioning. Finally, different weights are set to the coordinates of the shooting location with the maximum similarity and its 8 neighborhoods according to the number of matching points, and then interpolating calculation is applied to complete accurate positioning. Experimental results show that the accuracy of this study is less than 1.5 m and its running time is about 3.6 s. These are basically in line with the practical need. The described system has an advantage of low cost, high reliability and strong resistance to signal interference, so it has a better practical value as compared with visual odometry(VO) and radio frequency identification(RFID) based approach for vehicle positioning in the case of GPS not working.
A critical safe distance(CSD)model in V2V(vehicle-to-vehicle)communication systems was proposed to primarily enhance driving safety by disseminating warning notifications to vehicles when they approach calculated CSD.By elaborately analyzing the vehicular movement features especially when braking,our CSD definition was introduced and its configuration method was given through dividing radio range into different communication zones.Based on our definition,the needed message propagation delay was also derived which could be used to control the beacon frequency or duration.Next,the detailed CSD expressions were proposed in different mobility scenarios by fully considering the relative movement status between the front and rear vehicles.Numerical results show that our proposed model could provide reasonable CSD under different movement scenarios which eliminates the unnecessary reserved inter-vehicle distance and guarantee the safety at the same time.The compared time-headway model always shows a smaller CSD due to focusing on traffic efficiency whereas the traditional braking model generally outputs a larger CSD because it assumes that the following car drives with a constant speed and did not discuss the scenario when the leading car suddenly stops.Different from these two models,our proposed model could well balances the requirements between driving safety and traffic throughput efficiency by generating a CSD in between the values of the two models in most cases.
