Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
| Autor: | Lucas Eiermann, Ilja Radusch, Kay Massow, Gabi Breuel, Florian Wirthmuller |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences 0209 industrial biotechnology Computer science Process (engineering) Real-time computing Computer Science - Emerging Technologies Advanced driver assistance systems Systems and Control (eess.SY) 02 engineering and technology Solid modeling Electrical Engineering and Systems Science - Systems and Control Field (computer science) law.invention Computational Engineering Finance and Science (cs.CE) Computer Science - Robotics 020901 industrial engineering & automation law 0502 economics and business FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Radar Computer Science - Computational Engineering Finance and Science Representation (mathematics) 050210 logistics & transportation 05 social sciences Kalman filter Emerging Technologies (cs.ET) Robotics (cs.RO) Reflection mapping |
| Zdroj: | ICCP |
| Popis: | 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 able to contribute to increased traffic safety and driving comfort. Thus, we combine the mentioned research areas and show possible improvements, using the example of a threading process at a motorway access road. Furthermore, it is shown that already an average v2x equipment penetration of 80% can lead to a significant improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance compared to non v2x-equipped vehicles during the threading process. Comment: the article has been accepted for publication during the 16th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2020), 8 pages, 8 figures, 1 table |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|