Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

Application of Advanced Communication Technologies in the Improvement of Traffic Safety

Incorporation of advanced info-communication technologies into vehicular environment currently captures a large attention by numerous investigators, telecommunications operators, traffic safety regulatory institutions, car industry manufacturers and other interested participants. In this paper, we overview of some prospective wireless communication technologies, such as the DSRC (Dedicated Short Range Communications) and advanced LTE (Long Term Evolution) mobile communication systems, which are considered as two promising candidates to support future traffic safety applications in vehicular environment is presented. The communication requirements of some active traffic safety applications are pointed. A summary of various types of communications for intelligent VCS (Vehicular Communication System) applications is given. Some future directions and challenging issues for implementing traffic safety applications are also discussed. Our goal is to demonstrate the growing impact and importance of modern communication technologies in achieving future traffic accident-free roads.

Sensing Vehicle Dynamics Using Drivers Smart Phone to Keep away from Physical Damage

This paper utilizes Smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smart phones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Despite noisy sensor readings from Smartphone, our approach can achieve a classification accuracy of over 90 percent with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95 percent) with a lower false positive rate.