Adaptive cooperative maneuver planning algorithm for conflict resolution in diverse traffic situations

Düring, Michael; Franke, Kai; Balaghiasefi, Reza; Gonter, Mark; Belkner, Markus; Lemmer, Karsten

doi:10.1109/iccve.2014.7297550

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…Similarly, Brechtel et al [9] uses continuous POMDP in order to generate safe, efficient, and goal-directed driving behaviours for a two-vehicles interaction, although only two possible actions (change lane or stay) were considered. A different work includes non-cooperative road users [10], however, those additional road users are assumed to follow a single trajectory that is not modified by the ego-car's actions.…”

Section: Related Workmentioning

confidence: 99%

Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios

Garzón

Spalanzani

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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This paper presents a game theoretic decision making process for autonomous vehicles. Its goal is to provide a solution for a very challenging task: the merge manoeuvre in high traffic scenarios. Unlike previous approaches, the proposed solution does not rely on vehicle-to-vehicle communication or any specific coordination, moreover, it is capable of anticipating both the actions of other players and their reactions to the autonomous vehicle's movements. The game used is an iterative, multi-player level-k model, which uses cognitive hierarchy reasoning for decision making and has been proved to correctly model human decisions in uncertain situations. This model uses reinforcement learning to obtain a near-optimal policy, and since it is an iterative model, it is possible to define a goal state so that the policy tries to reach it. To test the decision making process, a kinematic simulation was implemented. The resulting policy was compared with a rule-based approach. The experiments show that the decision making system is capable of correctly performing the merge manoeuvre, by taking actions that require reactions of the other players to be successfully completed.

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Section: Related Workmentioning

confidence: 99%

Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios

Garzón

Spalanzani

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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show abstract

“…Therefore, traffic jams become more severe, especially in urban areas and on highways [2]. As a result, traffic efficiency, in addition to safety, gets increasingly important [3] to reduce or even avoid traffic jams. In this context, Vehicle-to-Everything (V2X) communication constitutes an important research area to increase traffic efficiency and safety for both human-driven and autonomous vehicles [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Bischoff¹,

Schiegg

Schuller³

et al. 2021

IEEE Access

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Cooperative driving is a promising approach to increase traffic efficiency and safety. However, cooperative driving requires high communication performance from the application perspective when coordinating and executing a cooperative maneuver, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized vehicular networks, e. g., from the European Telecommunications Standard Institute, maintain the radio communication performance under congested channel conditions but can severely degrade the communication performance from the application perspective. In this paper, we propose a collaborative resource allocation mechanism with content-aware congestion control for decentralized vehicular networks that prioritizes vehicles with relevant information under congested channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-agnostic congestion control mechanisms.

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Adaptive Heterogeneous V2X Communication for Cooperative Vehicular Maneuvering

Bischoff¹,

Schiegg

Meuser

et al. 2021

Communications in Computer and Information Science

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Adaptive cooperative maneuver planning algorithm for conflict resolution in diverse traffic situations

Cited by 11 publications

References 17 publications

Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios

Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Adaptive Heterogeneous V2X Communication for Cooperative Vehicular Maneuvering

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