Vehicular networking and computer vision-based distance estimation for VANET application using Raspberry Pi 3

This research was implementing vehicle networking using WIFI connection and computer vision to measure the distance of vehicles in front of a driver. In particular, this works aimed to improve a safe driving environment thus supporting the current technology concept being developed for inter-vehicular networking, VANET, especially in its safety application such as Overtaking Assistance System. Moreover, it can wirelessly share useful visual information such as hazard area of a road accident. In accordance with Vehicle-to-Vehicle (V2V) concept, a vehicle required to be able to conduct networking via a wireless connection. Useful data and video were the objects to be sent over the network established. The distance of a vehicle to other vehicles towards it is measured and sent via WIFI together with a video stream of the scenery experienced by the front vehicle. Haar Cascade Classifier is chosen to perform the detection. For distance estimation, at least three methods have been compared in this research and found evidence that, for measuring 5 meters, the iterative methods shows 5.80. This method performs well up to 15 meters. For measuring 20 meters, P3P method shows a better result with only 0.71 meters to the ground truth. To provide a physical implementation for both the detection and distance estimation mechanism, those methods were applied in a compact small-sized vehicle-friendly computer device the Raspberry Pi. The performance of the built system then analyzed in terms of streaming latency and accuracy of distance estimation and shows a good result in measuring distance up to 20 meters

Risk assessment for traffic safety applications with V2V communications

Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metric

Risk assessment for traffic safety applications with V2V communications

Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metric