Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Coll-Perales, Baldomero; Schulte-Tigges, Joschua; Rondinone, Michele; Gozálvez, Javier; Reke, Michael; Matheis, Dominik; Walter, Thomas

doi:10.1109/tits.2021.3061085

Cited by 13 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coll-Perales et al [14] address the problem of multiple vehicles engaging in MRM at the same time within the same area due to a failure of transfer of control (ToC). The authors investigate the impact on traffic safety and efficiency of MRM execution when triggered by decentralised environmental notification message (DENM) based or manoeuvre coordination message (MCM) based ToC management schemes.…”

Section: B Cooperative and Collaborative Vehicle Domainmentioning

confidence: 99%

Minimal Risk Manoeuvre Strategies for Cooperative and Collaborative Automated Vehicles

Vu,

Warg,

Thorsén

et al. 2023

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

View full text Add to dashboard Cite

During the last decade, there has been significant increase in research focused on automated vehicles (AVs) and ensuring safe operation of these vehicles. However, challenges still remain, some involving the cooperation and collaboration of multiple AVs, including when and how to perform a minimal risk manoeuvre (MRM), leading to a minimal risk condition (MRC) when an AV within one of these systems is unable to complete its original goal. As most literature is focused on individual AVs, there is a need to adapt and extend the knowledge and techniques to these new contexts. Based on existing knowledge of individual AVs, this paper explores MRM strategies involving cooperative and collaborative AV systems with different capabilities. Specifically, collaborative systems have the potential to enact local MRCs, allowing continued productivity despite having one (or several) of its constituents encounter a fault. Definitions are provided for local and global MRCs, alongside discussions of their implications for MRMs. Illustrative examples are also presented for each type of system.

show abstract

Section: B Cooperative and Collaborative Vehicle Domainmentioning

confidence: 99%

Minimal Risk Manoeuvre Strategies for Cooperative and Collaborative Automated Vehicles

Vu,

Warg,

Thorsén

et al. 2023

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

View full text Add to dashboard Cite

show abstract

“…2 could be at least postponed to a higher demand, or, in the best case, be avoided at all by AV specific ToR scheduling. Subsequently, the management of downward ToCs and guidance of MRMs to safe spots has been explored via real-world testing by [32]. Their results suggested that the provision of personalized advice to CAVs including information about recommended ToC and safe spot locations could minimize MRMs taking place in lane which can result in hazardous situations.…”

Section: Related Work a Transitions Of Controlmentioning

confidence: 99%

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Alms

Noulis

Mintsis

et al. 2022

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.Index Terms-connected automated vehicles (CAV), reinforcement learning (RL), take-over request (ToR), traffic management (TM), transition of control (ToC) NOMENCLATURE AV Automated vehicle CAV Connected automated vehicle CV Connected vehicle LoD Level of demand MDP Markov decision process MRM Minimum risk manoeuvre MV Manual vehicle No-AD No automated driving RL Reinforcement learning RSI Roadside infrastructure TM Traffic management TMC Traffic management center ToC Transition of control ToR Take-over request

show abstract

“…To guarantee safety in emergencies, a minimum risk maneuver (MRM) mode is implemented in self-driving systems [13], [14]. In the self-driving industry, the risk avoidance state in the event of a deadline miss is called a minimum risk condition (MRC), and the steering wheel operation and brake control until the vehicle moves to MRC is called MRM.…”

Section: Introductionmentioning

confidence: 99%

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Yano

Azumi

2023

IEEE Access

View full text Add to dashboard Cite

Self-driving systems have a hard real-time nature, and the control commands of the vehicle must be output before the end-to-end deadline using sufficiently new data. It must also have the functionality to quickly shift to a safe state in the event of a deadline miss. However, the current self-driving system can only detect a deadline miss at the end of the process. To further improve safety, a method for detecting the possibility of a deadline miss in the middle of the process is required. Therefore, we represent such a real-time system as a mixed timer-driven and event-driven directed acyclic graph (DAG) and propose an early detection method for deadline misses by deriving a time constraint for each node. The experimental evaluation shows that the proposed method can detect deadline misses early for various scheduling algorithms. It also shows that the deadline miss ratio can be reduced by prioritizing the scheduling of jobs with a small margin to the time constraint determined using the proposed method for each job.INDEX TERMS DAG, deadline miss early detection, event-driven task, self-driving system, timer-driven task

show abstract

Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Cited by 13 publications

References 12 publications

Minimal Risk Manoeuvre Strategies for Cooperative and Collaborative Automated Vehicles

Minimal Risk Manoeuvre Strategies for Cooperative and Collaborative Automated Vehicles

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Contact Info

Product

Resources

About