Effective Safety Message Dissemination with Vehicle Trajectory Predictions in V2X Networks

Abstract

Exploring data connection information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications using advanced machine learning approaches, an intelligent transportation system (ITS) can provide better safety services to mitigate the risk of road accidents and improve traffic efficiency. In this work, we propose an end-edge-cloud architecture to deploy machine learning-driven approaches at network edges to predict vehicles’ future trajectories, which is further utilized to provide an effective safety message dissemination scheme. With our approach, the traffic safety message will only be disseminated to relevant vehicles that are predicted to pass by accident areas, which can significantly reduce the network data transmission overhead and avoid unnecessary interference. Depending on the vehicle connectivity, our system adaptively chooses vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications to disseminate safety messages. We evaluate the system by using a real-world VANET mobility dataset, and experimental results show that our system outperforms other mechanisms without considering any predicted vehicle trajectory density information.

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

“…It should also be noted that the macroscopic traffic flow behaviors could change based on the penetration rate of vehicles with traffic flow-focused algorithm or control policy design. For example, analyses based on experimental data offers insight into how the fundamental diagram of traffic flow could change based on various levels of penetration of AVs, with potential higher flow rates with increased penetration rate [ 29 , 57 ]. However, there seems to be a lack of consensus on the effects of connected autonomous vehicles (CAVs) on traffic flow [ 58 ].…”

“…Similarly, context-aware algorithms have been created to select intermediate nodes for message delivery according to various criteria including message importance and current locations of the vehicles [27]. More recent works have sought to develop methods that can send relevant notifications, such as by using vehicular density-based metrics to inform vehicles that may pass through an accident zone [28,29], or by using probabilistic prediction-based messages for re-routing vehicles based on potential encounters with traffic events [30]. An excellent overview of the topics related to ZOR and geo-casting protocols can be found in [31].…”

Position-Based Emergency Message Dissemination Schemes in the Internet of Vehicles: A Review

“…10) Trajectory Prediction and Traffic Condition Based Scheme: Li et al in [106] introduced an approach in which the message is only disseminated by the vehicle predicted to pass through the accident site to avoid excessive delay and message overhead. A hybrid early warning message system for VANETs in sparse and dense scenarios is organized to deliver an alert message to relevant vehicles based on vehicle trajectory prediction for reliable delivery.…”