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Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions

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Document pages: 8 pages

Abstract: Modern driver assistance systems as well as autonomous vehicles take theirdecisions based on local maps of the environment. These maps include, forexample, surrounding moving objects perceived by sensors as well as routes andnavigation information. Current research in the field of environment mapping isconcerned with two major challenges. The first one is the integration ofinformation from different sources e.g. on-board sensors like radar, camera,ultrasound and lidar, offline map data or backend information. The secondchallenge comprises in finding an abstract representation of this aggregatedinformation with suitable interfaces for different driving functions andtraffic situations. To overcome these challenges, an extended environment modelis a reasonable choice. In this paper, we show that role-based motionpredictions in combination with v2x-extended environment models are able tocontribute to increased traffic safety and driving comfort. Thus, we combinethe mentioned research areas and show possible improvements, using the exampleof a threading process at a motorway access road. Furthermore, it is shown thatalready an average v2x equipment penetration of 80 can lead to a significantimprovement of 0.33m s^2 of the total acceleration and 12m more safety distancecompared to non v2x-equipped vehicles during the threading process.

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