Dipl.-Ing.

Jiru, Josef

Filters

2 1
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Preventing Failures of Cooperative Maneuvers Among Connected and Automated Vehicles
    ( 2022-09)
    Häfner, Bernhard
    ;
    Jiru, Josef
    ;
    Schepker, Henning F.
    ;
    Schmitt, Georg Albrecht
    ;
    Ott, Jörg
    Automated vehicles will be able to drive autonomously in various environments. An essential part of that is to predict other vehicles’ intents and to coordinate maneuvers jointly. Such cooperative maneuvers have the ability to make driving safer and traffic more efficient. However, among the various communication protocols proposed for maneuver coordination, no single one satisfies all requirements. This paper assesses failure risks and mitigation strategies for cooperative maneuvers, including an analysis of popular protocols regarding this aspect. We evaluate two cooperation protocols representative for different approaches to cooperation, the complex vehicular interactions protocol as well as trajectory sharing, concerning the performance of mitigation mechanisms and their influence on maneuver success rates or times to reach consensus among maneuver participants. Via simulation, we show that both are suitable for cooperative maneuvers in realistic scenarios and investigate the trade-offs individual mitigation mechanisms face. These results are well-suited as guidelines and benchmark for other researchers developing cooperative maneuver protocols.
  • Publication
    Proposing Cooperative Maneuvers Among Automated Vehicles Using Machine Learning
    ( 2021)
    Häfner, Bernhard
    ;
    Jiru, Josef
    ;
    Schepker, Henning
    ;
    Schmitt, Georg Albrecht
    ;
    Ott, Jörg
    Cooperative maneuvers will enable automated vehicles to optimize traffic flow and increase safety via vehicle-to-vehicle communication. Different approaches and protocols exist, but no study has investigated how to generate intelligent suggestions for cooperative maneuvers. We use machine learning to propose safe and suitable overtake maneuvers. To this end, we train a classifier for maneuver success as well as regression models on an extensive data set of randomized initial situations. In addition, we show that changing objective functions allows optimizing for different goals like smoothness or driven distance. Our evaluation shows that machine learning is well-suited to suggest cooperative maneuvers while also facing some trade-offs. This work may thus provide a benchmark for advanced studies on cooperative maneuver proposals.
  • Publication
    Preventing Failures of Cooperative Maneuvers Among Connected and Automated Vehicles
    ( 2021)
    Häfner, Bernhard
    ;
    Jiru, Josef
    ;
    Schepker, Henning
    ;
    Schmitt, Georg Albrecht
    ;
    Ott, Jörg
    Automated vehicles will be able to drive autonomously in various environments. An essential part of that is to predict other vehicles' intents and to coordinate maneuvers jointly. Such cooperative maneuvers have the ability to make driving safer and traffic more efficient. However, among the various communication protocols proposed for maneuver coordination, no single one satisfies all requirements.This paper assesses failure risks and mitigation strategies for cooperative maneuvers, including an analysis of popular protocols regarding this aspect. Next, we evaluate one particular cooperation protocol, the complex vehicular interactions protocol (CVIP), concerning performance of mitigation mechanisms and their influence on maneuver success rates or times to reach consensus among maneuver participants. Via simulation, we show that CVIP is suitable for cooperative maneuvers in realistic scenariosand investigate the trade-offs individual mitigation mechanisms face. These results are well-suited as guidelines and benchmark for other researchers developing cooperative maneuver protocols.