2015/06/01 by Dennis Boehmlaender, Sinan Hasirlioglu, Vitor Yano +3 · 1 citation
Engineering · Computer Science · #Autonomous Vehicle Technology and Safety #Video Surveillance and Tracking Methods #Anomaly Detection Techniques and Applications
paper · doi:10.1109/dsn-w.2015.23
openalex publication_date 2015/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
The paper discusses a new approach in contactless crash detection combining measurements of vehicle dynamics, exteroceptive sensors and vehicle-to-vehicle (V2V) communication data. The proposed architecture aims to activate vehicle safety functions prior an imminent collision to minimize the risk of suffering a major injury. An activation needs a precise prediction of time to collision (TTC), the crash severity (Cs) and other relevant crash parameters. This paper studies the contribution of V2V communication data to predict potential collisions and to realize a reliable activation. An algorithm is presented, that merges fused measurements of a video camera, a laser range finder (LRF) and ego vehicle motion sensors with V2V communication data to predict collisions. The benefit using V2V communication is demonstrated by evaluating collision prediction errors. This analysis is carried out based on experimental data produced by two scale model vehicles.