Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

<div>Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety.</div> <div>This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes.</div> <div>We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements on off-board perception systems, the communication network, and AV tactical safety behavior. The methodology is instantiated as a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts.</div> <div>We show how the age of information and observation uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development.</div>

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Publication Details

Journal
SAE International Journal of Connected and Automated Vehicles
Published
2025-02-24
DOI
https://doi.org/10.4271/12-08-02-0020
Citations
1
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.68
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article

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

Martin Törngren, José Manuel Gaspar Sánchez, Gianfilippo Fornaro
1 citations
SAE International Journal of Connected and Automated Vehicles
Autonomous Vehicle Technology and Safety
0.68
article

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

Martin Törngren, José Manuel Gaspar Sánchez, Gianfilippo Fornaro
article en
1 citations

Abstract

<div>Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety.</div> <div>This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes.</div> <div>We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements on off-board perception systems, the communication network, and AV tactical safety behavior. The methodology is instantiated as a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts.</div> <div>We show how the age of information and observation uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development.</div>

SAE International Journal of Connected and Automated VehiclesVol. 8(2)
KTH Royal Institute of Technology (SE)
Openalex Percentile: Top 34%
Autonomous Vehicle Technology and Safety
0.68
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