Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Lucas Eiermann, Florian Wirthm\"uller, Kay Massow, Gabi Breuel and, Ilja Radusch

TL;DR
This paper presents an environment model extended through V2X communication and role-based behavior predictions to enhance safety and comfort in on-ramp merging, demonstrating significant improvements with partial V2X deployment.
Contribution
It introduces a novel approach combining role-based motion predictions with V2X-extended environment models for improved traffic safety and driving comfort during on-ramp merging.
Findings
80% V2X penetration yields 0.33 m/s^2 acceleration improvement
12 meters increased safety distance during merging
Enhanced environment models contribute to safer, more comfortable driving
Abstract
Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e.g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this aggregated information with suitable interfaces for different driving functions and traffic situations. To overcome these challenges, an extended environment model is a reasonable choice. In this paper, we show that role-based motion predictions in combination with v2x-extended environment models are…
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