Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

Song, Yu; Chitturi, Madhav; Noyce, David A.

doi:10.1016/j.aap.2022.106814

Cited by 17 publications

(4 citation statements)

References 33 publications

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“…Transportation agencies and policymakers can leverage the insights gained from this study to prioritize infrastructure improvements, identify hazardous driving behaviors and blackpoint roadway locations, and allocate resources effectively for traffic enforcement [59,60]. Additionally, automotive manufacturers can utilize the identified determinants of injury risk to inform the design of vehicle structures and incorporate advanced safety technologies that address specific crash scenarios and impact positions [61,62]. By translating the research findings into practical applications, this study contributes to the ongoing efforts aimed at enhancing road safety and mitigating the human and economic costs associated with crash injuries.…”

Section: Discussionmentioning

confidence: 99%

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

et al. 2023

Applied Sciences

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Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system.

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Section: Discussionmentioning

confidence: 99%

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

et al. 2023

Applied Sciences

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“…For instance, in a network there may be some junctions that are more prone to traffic accidents due to their topology or geographical position, hence should not receive a significant amount of traffic. Further, there could be some links that are not suitable for some large vehicles as they are particularly narrow or steep [22], [23]. Dynamic risks are instead dependent on the time at which the vehicle is expected to navigate an area.…”

Section: Risk-awarenessmentioning

confidence: 99%

Sustainability Opportunities and Ethical Challenges of AI-Enabled Connected Autonomous Vehicles Routing in Urban Areas

Guo,

Vallati,

Wang

et al. 2024

IEEE Trans. Intell. Veh.

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The advent of Connected Autonomous Vehicles (CAVs) paves the way to a new era of urban traffic control and management, driven by Artificial Intelligence (AI)-enabled strategies. This advancement promises significant improvements in infrastructure use optimisation, traffic delay reduction, and overall sustainability. The autonomous driving capabilities of CAVs, coupled with the communication technology, allow vehicles to play an active role in urban traffic control: they can follow tailored instructions and can act as highly accurate moving sensors for traffic authorities. However, such improved capabilities come at the cost of unprecedented vulnerabilities to cyber exploitation, and with the concrete potential to increase social and economic disparities. As an extension of TIV-DHW (Distributed/Decentralised Hybrid Workshop) on ERS (Ethics, Responsibility, and Sustainability), this letter explores how the AI-enabled routing methodology can enhance urban transportation sustainability while also discussing the ethical implications and challenges it presents.

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“…Traditional test scenarios are generated manually and are relatively inefficient [28,29]. At the same time, when various scenarios and complex parameters are tested, the amount of testing is relatively large, and the manual generation method described above consumes significant manpower [30]. Against this background, the present study proposes an automatic scenario generation method to realize automatic scene generation and batch testing.…”

Section: Automatic Generation Of Test Scenariosmentioning

confidence: 99%

An Evaluation Method for Automated Vehicles Combining Subjective and Objective Factors

Wang

et al. 2023

Machines

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The rapid development of automated vehicle technology requires reasonable test scenarios and comprehensive evaluation methods. This paper proposes an evaluation method for automated vehicles combining subjective and objective factors. First, we propose a method for automatically generating test scenarios and for batch testing autonomous vehicles. Then, the use of the target layer, total index layer, and index layer of automated vehicles is proposed to establish a more comprehensive evaluation system for automated vehicles. Specifically, the analytic hierarchy process (AHP, subjective) and improved criteria importance though intercriteria correlation (CRITIC, objective) methods are used to determine the weight of the indicators, and a two-level fuzzy comprehensive (subjective and objective) evaluation method is adopted to comprehensively evaluate the performance of the automated vehicles. Finally, the effectiveness of the proposed evaluation method combining subjective and objective factors is verified through virtual simulations and real-world experiments. Through a combination of subjective and objective methods, improved results can be obtained for safety, efficiency, economy, intelligence, and comfort tests.

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Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

Cited by 17 publications

References 33 publications

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

Sustainability Opportunities and Ethical Challenges of AI-Enabled Connected Autonomous Vehicles Routing in Urban Areas

An Evaluation Method for Automated Vehicles Combining Subjective and Objective Factors

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