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Cited by 18Traffic control and managementTraffic and Road SafetyAutonomous Vehicle Technology and Safety

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

Open access
Kim, Hoseon, Ko, Jieun, Oh, Cheol, Kim, Seoungbum·2024·Sustainability
DOI 10.3390/su16229672
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Abstract

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

AI snapshot

Written from the paper’s full text.

TL;DR

This study uses VISSIM simulations of the Seoul autonomous mobility testbed to identify promising AV driving-safety indicators by operational design domain (ODD) and rank ODDs by risk in mixed traffic. It finds unsignalized left turns, signalized right turns, and roundabouts among the highest-risk ODDs, with crosswalks and bicycle lanes also high-risk due to interactions with non-vehicular road users.

Key findings

  • Time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left-turn and straight-through signalized intersections among intersection-related ODDs.
  • The highest risk scores were unsignalized left turns (0.554), signalized right turns (0.525), and roundabouts (0.501); intersection crosswalks, mid-block crosswalks, and bicycle lanes scored 0.552, 0.485, and 0.473, respectively.
  • Among vehicle-interaction ODDs, minor road merging and illegal parking had risk scores of 0.493 and 0.457, above the reported average; signalized straight-through had the lowest reported risk score at 0.135.
  • ODDs involving pedestrians and cyclists were high-risk because they require rapid AV speed and trajectory adjustments to unpredictable non-vehicular movements.

Methods

  • Built a VISSIM simulation of the Seoul autonomous mobility testbed using two routes of 5.3 km and 4.0 km, segmented into 30-m units for 219 sections, and analyzed 16 ODDs.
  • Used automated vehicle data (AVD) from five AVs collected 10 February 2022–31 October 2022, totaling 6,243,872 records (3,238,883 AD mode; 3,004,989 MD mode), plus traffic data; main road traffic was set to 1180 vph, average bus arrival interval to 176 s, and observed flows of 180 pedestrians/hour and 80 bicycles/hour.
  • Evaluated 7 longitudinal and 9 interaction indicators, applied PCA with KMO > 0.6, Bartlett’s test p < 0.05, and commonality > 0.5, then normalized promising indicators and averaged them into a risk score; simulation ran 2 h excluding 30 min warm-up, with 10 random seeds.

Limitations

  • The paper states the simulation is based on the real world but real-world conditions have more unexpected variables, so future research should validate results with AV driving data.
  • The paper states it only considers AV driving safety, not the safety of neighboring vehicles, pedestrians, and cyclists; future work should develop a more comprehensive AV safety framework.
  • The paper states ODDs were selected based on local Seoul autonomous mobility testbed characteristics, so future research should expand to wider ODD environments, traffic volumes, vehicle types, and complex interactions.
  • Evident from the text: risk rankings depend on PCA-selected indicators and simulation calibration, and the study does not report real-world AV crash or conflict outcomes for validation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/su16229672

Cites

54 of 55 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

7 of 33 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient.

    “However, several studies have been published on the performance of ADS-mounted sensors, but research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient [16][17][18][19].”
    Quote in the paperSubstantial claim2 of 4 sources checked
    • cites [16] Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G.P., and Cho, W. (2022). A review on autonomous vehicles: Progress, methods and challenges. Electronics, 11. source

      Section 3.1 defines ODD and relates it to an automated driving system’s operating conditions and safety responses. Section 6 discusses research gaps, but does not say that research measuring ODD’s impact on AV driving safety is insufficient. Searches for that specific gap found no passage addressing it. (LLM, conf=0.72)

      Experimental — accuracy not yet measured
    • cites [17] Li, 2022, Decision Making of Autonomous Vehicles in Lane Change Scenarios: Deep Reinforcement Learning Approaches with Risk Awareness, Transp. Res. Part C Emerg. Technol. source
    • cites [18] Wang, 2020, Safety of Autonomous Vehicles, J. Adv. Transp. source

      The Introduction (page unavailable) says an updated statistical perspective on AV safety is missing from the literature. That supports a general research gap, but the paper does not say that research quantifying the effect of operational design domains (ODDs) on driving safety is insufficient. (LLM, conf=0.72)

      Experimental — accuracy not yet measured
    • cites [19] Kang, 2023, A Data-driven Control Policy based Driving Safety Analysis System for Autonomous Vehicles, IEEE Internet Things J. source
  • Premisecitation

    The roadway will continue to have mixed traffic with vehicles of different ADS levels for a considerable period.

    “In addition, the roadway will continue to have mixed traffic with vehicles of different ADS levels for a considerable period [2][3][4][5][6][7].”
    Quote in the paperSubstantial claim4 of 6 sources checked
    • cites [2] Bahrami, 2020, Optimal traffic management policies for mixed human and automated traffic flows, Transp. Res. Part A Policy Pract. source
    • cites [3] Li, 2022, Trajectory planning for autonomous modular vehicle docking and autonomous vehicle platooning operations, Transp. Res. Part E Logist. Transp. Rev. source
    • cites [4] Rad, 2020, Design and operation of dedicated lanes for connected and automated vehicles on motorways: A conceptual framework and research agenda, Transp. Res. Part C Emerg. Technol. source

      The Introduction (page unavailable) identifies mixed traffic on motorways, with connected or automated vehicles sharing the road with manually driven SAE level 0 vehicles. It also describes different automation levels and says level 3 and 4 consumer vehicles are expected in the coming decade, but it does not explicitly predict how long mixed traffic will continue. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [5] Tengilimoglu, 2023, Implications of automated vehicles for physical road environment: A comprehensive review, Transp. Res. Part E Logist. Transp. Rev. source
    • cites [6] Yu, 2022, Will all autonomous cars co-operate? Brands’ strategic interactions under dynamic congestion, Transp. Res. Part E Logist. Transp. Rev. source
    • cites [7] Zhu, 2022, Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas, Multimodal Transp. source

      The Introduction discusses mixed traffic comprising connected and autonomous vehicles (CAVs) and human-driven vehicles, and the Conclusion identifies adapting the strategy to such traffic as future research. Neither passage predicts that mixed traffic will persist for a considerable period or describes a roadway mix of vehicles at different ADS levels. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Findingown data

    A total of 16 ODDs were identified through field surveys, including straight, left, and right turns at both signalized and unsignalized intersections.

    “A total of 16 ODDs were identified through field surveys, including straight, left, and right turns at both signalized and unsignalized intersections.”
    Quote in the paperNot a substantial claim
  • Findingnone given

    This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV).

    “This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.
  • Findingreasoning

    The VF headway time for U-turns, additional lane segments, illegal parking, bus stops, and merging lane was derived as driving safety in terms of interaction between vehicles.

    “The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.
  • May combine two claims. 18
  • Classified as probably not a substantial claim. 26
  • The quote only partly states this claim. 2

  • May combine two claims.

    Premisecitation

    VF is capable of quantitatively analyzing irregular changes in indicators over time and has been reported to be effectively used for driving behavior analysis.

    “VF is capable of quantitatively analyzing irregular changes in the indicators over time and has been reported to be effectively used for driving behavior analysis [46,47].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [46] Kamrani, 2018, Extracting useful information from basic safety message data: An empirical study of driving volatility measures and crash frequency at intersections, Transp. Res. Rec. source
    • cites [47] Mahdinia, 2021, Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles, Accid. Anal. Prev. source

  • May combine two claims.

    Findingreasoning

    Intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores.

    “Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The road network for the Sangam AV testbed consists of two routes measuring 5.3 km and 4.0 km in length.

    “The two routes measure 5.3 km and 4.0 km in length.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The average bus arrival interval in the analysis area is 176 s.

    “The average bus arrival interval is 176 s.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Pedestrian and bicycle traffic volumes were observed at 80 bicycles per hour and 180 pedestrians per hour.

    “Pedestrian and bicycle traffic volumes were collected through one-hour field surveys, with observed flows of 80 bicycles per hour and 180 pedestrians per hour.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively.

    “The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

  • May combine two claims.

    Findingown data

    The risk scores for intersection crosswalks, mid-block crosswalks, and bicycle lanes were 0.552, 0.485, and 0.473, respectively, and have higher risk rankings than the average risk score.

    “The risk scores for intersection crosswalks, mid-block crosswalks, and bicycle lanes, which are ODDs involving interactions with non-vehicular road users, are 0.552, 0.485, and 0.473, respectively, and have higher risk rankings than the average risk score.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The risk scores for the minor road merging section and the section with illegal parking were 0.493 and 0.457, respectively, which are higher than the average risk score.

    “The risk scores for the minor road merging section and the section with illegal parking were calculated to be 0.493 and 0.457, respectively, which are higher than the average risk score.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingnone given

    The ODD vulnerability analysis produced a ranking of ODDs by risk score.

    “The ODD vulnerability ranking highlights the need for risk-aware planning in mixed CAV/HV environments.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    The risk scores for straight through and left turns at signalized intersections are 0.135 and 0.369, respectively.

    “The risk scores for straight through and left turns at signalized intersections are 0.135 and 0.369, respectively.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The risk scores for right turns at signalized intersections and roundabouts were 0.525 and 0.501, respectively, which is higher than the average risk score of 0.408 for all ODDs.

    “The risk scores for right turns at signalized intersections and roundabouts were found to be 0.525 and 0.501, respectively, which is higher than the average risk score of 0.408 for all ODDs.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Autonomous vehicles have the potential to effectively reduce accidents caused by human factors.

    “Autonomous vehicles (AVs) have the potential to effectively reduce accidents caused by human factors [1].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] NHTSA (2018). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey, National Highway Traffic Safety Administration. Available online: https://trid.trb.org/View/1507603.

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    PCA was considered feasible if the KMO measure was greater than 0.6 and the Bartlett's test of sphericity p-value was less than 0.05.

    “PCA was considered feasible if the KMO measure was greater than 0.6 and the Bartlett's test of sphericity p-value was less than 0.05 [41][42][43][44].”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [41] Kaiser, 1974, An index of factorial simplicity, Psychometrika source
    • cites [42] Bartlett, 1951, The effect of standardization on a χ2 approximation in factor analysis, Biometrika source
    • cites [43] Howard, 2023, A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement, J. Bus. Res. source
    • cites [44] Salehimanesh, 2020, Factor analysis of the Persian version of the voice disability coping questionnaire, J. Voice source

  • May combine two claims.

    Interpretationown data

    Unsignalized left turns, signalized right turns, and roundabouts are the most vulnerable ODDs for AVs.

    “indicating these as the most vulnerable ODDs for AVs.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

  • May combine two claims.

    Interpretationreasoning

    Intersection and mid-block crosswalks, as well as bicycle lanes, are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory.

    “These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The study focused on the most common ODD scenarios to ensure general applicability and relevance, avoiding atypical or rare configurations.

    “The study focused on the most common ODD scenarios to ensure general applicability and relevance, avoiding atypical or rare configurations.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The selected ODDs are representative of urban AV driving safety scenarios.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The simulation data was collected 10 times by applying different random seeds of the same scenario to increase the reliability of the simulation results.

    “In addition, the simulation data was collected 10 times by applying different random seeds of the same scenario to increase the reliability of the simulation results.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Current research has mainly focused on the capability of AV sensors under specific conditions, without fully reflecting the variety of situations AVs will face in the real world, including complex road geometries and transportation infrastructure.

    “Current research has mainly focused on the capability of AV sensors under specific conditions, without fully reflecting the variety of situations AVs will face in the real world, including complex road geometries and transportation infrastructure.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Previous studies have focused on the performance of the sensors and vehicle behavior control equipped with ADS.

    “Several studies have suggested and analyzed various methods to evaluate the performance of AVs, however, the previous studies have focused on the performance of the sensors and vehicle behavior control equipped with ADS.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Study design and conductnone given

    The driving behavior of AVs was implemented in the simulation to replicate the operational characteristics of real-world AVs.

    “The driving behavior of AVs was implemented in the simulation to replicate the operational characteristics of real-world AVs.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The simulation environment replicates real-world AV operational characteristics.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The results were used to optimize the behavior parameters and traffic speed distribution.

    “The results were used to optimize the behavior parameters and traffic speed distribution.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Unstable traffic flow can arise from differences in cognitive and judgment abilities between AVs and non-AVs, as well as from driver behavior characteristics influenced by ODD.

    “Numerous studies have concluded that unstable traffic flow can arise from differences in cognitive and judgment abilities between AVs and non-AVs, as well as from driver behavior characteristics influenced by ODD [11][12][13].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [11] Jin, 2020, Modeling and Stability Analysis of Mixed Traffic with Conventional and Connected Automated Vehicles from Cyber Physical Perspective, Phys. A Stat. Mech. Appl. source
    • cites [12] Pan, 2024, The impacts of connected autonomous vehicles on mixed traffic flow: A comprehensive review, Phys. A Stat. Mech. Appl. source
    • cites [13] Rong, 2011, Effects of individual differences on driving behavior and traffic flow characteristics, Transp. Res. Rec. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    TTC considers a serious collision to have occurred if the remaining time before a collision is less than 1.5 s when both vehicles are driving in the same direction and at the same speed as they are currently driving.

    “The TTC considers a serious collision to have occurred if the remaining time before a collision occurs is less than 1.5 s if both vehicles are driving in the same direction and at the same speed as they are currently driving [48].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] FHWA (2003). Surrogate Safety Measures from Traffic Simulation Models, FHWA. Federal Highway Administration Report FHWA-RD-03-050.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The behavior of AD and MD mode were implemented using AVD to analyze driving behavior by driving mode.

    “The behavior of AD and MD mode were implemented using AVD to analyze driving behavior by driving mode.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    The implementation of AVs has the potential to improve traffic safety fundamentally by eliminating traffic accidents caused by human error.

    “In other words, the implementation of AVs has the potential to improve traffic safety fundamentally by eliminating traffic accidents caused by human error.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    A commonality above 0.5 can be assumed to be a significant variable.

    “A commonality above 0.5 can be assumed to be a significant variable [45].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [45] Willgoss, 2013, The Development and Validation of the Anxiety Inventory for Respiratory Disease, Chest source

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Studies analyzing the driving safety aspects of AVs do not provide validation or validity of the indicators used in the analysis.

    “In addition, the studies analyzing the driving safety aspects of AVs do not provide validation or validity of the indicators used in the analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles.

    “In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.
    • Rests on an assumption: The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.
    • Rests on an assumption: The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

  • Classified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.

    Interpretationnone given

    Left turns at unsignalized intersections have a relatively large number of conflict points, which increases the possibility of conflicts.

    “In particular, the scenario of performing a left turn has a relatively large number of conflict points, which increases the possibility of conflicts.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationnone given

    This study proposed a new approach that evaluates the driving safety vulnerability of AVs by comprehensively analyzing each ODD's characteristics.

    “This study proposed a new approach that evaluates the driving safety vulnerability of AVs by comprehensively analyzing each ODD's characteristics.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Intersection and mid-block crosswalks are characterized by frequent pedestrian conflicts, which may cause AVs to decelerate abruptly.

    “Intersection and mid-block crosswalks are characterized by frequent pedestrian conflicts, which may cause AVs to decelerate abruptly.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    DRAC defines a conflict if the collision avoidance deceleration exceeds 3.35 m/s2 when the following vehicle recognizes the hazard ahead and begins to slow down.

    “The DRAC is defined as a conflict if the collision avoidance deceleration exceeds 3.35 m/s 2 when the following vehicle recognizes the hazard ahead and begins to slow down [50].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [50] Archer, J. (2005). Indicators for Traffic Safety Assessment and Prediction and Their Application in Micro-Simulation Modelling: A Study of Urban and Suburban Intersections. [Ph.D. Thesis, KTH Royal Institute of Technology]. ISBN: 91-7323-119-3.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Right turns at signalized intersections involve complex interactions with other vehicles and a relatively increased potential for conflicts with pedestrians.

    “Right turns at signalized intersections involve complex interactions with other vehicles, as well as a relatively increased potential for conflicts with pedestrians.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given

    The simulation environment in this study is based on the real world.

    “the simulation environment in this study is based on the real world”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    In complex traffic flow situations with vehicles entering and turning from multiple directions, AVs require continuous decision-making.

    “In complex traffic flow situations with vehicles entering and turning from multiple directions, AVs require continuous decision-making.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CPI is the probability that the DRAC of the following vehicle is greater than the vehicle's specific maximum deceleration in a vehicle following situation.

    “In addition, CPI is the probability that the DRAC of the following vehicle is greater than the vehicles specific maximum deceleration in a vehicle following situation [51].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [51] Cunto, 2008, Calibration and validation of simulated vehicle safety performance at signalized intersections, Accid. Anal. Prev. source

  • May combine two claims.

    Interpretationown data

    High-risk scores suggest that unsignalized left turns, signalized right turns, and roundabouts are particularly vulnerable in terms of driving safety.

    “Such high-risk scores suggest these ODDs are particularly vulnerable in terms of driving safety.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study only considers the driving safety aspect of AVs.

    “The second limitation is that it only considers the driving safety aspect of AVs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    SDI is a conflict measurement indicator based on the difference between the minimum stopping distance of the leading and following vehicles, and a conflict can be estimated if the stopping distance of the trailing vehicle is longer than the leading vehicle.

    “SDI is a conflict measurement indicator based on the difference between the minimum stopping distance of the leading and following vehicles.If the stopping distance of the trailing vehicle is longer than the leading vehicle, it can be estimated that a conflict has occurred [49].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [49] Oh, 2006, A Method for Identifying Rear-end Collision Risks Using Inductive Loop Detectors, Accid. Anal. Prev. source

  • May combine two claims.

    Interpretationown data

    Risk scores higher than the average risk score for all ODDs indicate unsafe conditions leading to greater potential of accident occurrence.

    “These scores are higher than the average risk score for all ODDs analyzed in this study, indicating unsafe conditions leading to greater potential of accident occurrence.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The ODDs selected in this study were analyzed based on local characteristics of traffic conditions in the Seoul autonomous mobility testbed.

    “the ODDs selected in this study were analyzed based on local characteristics of traffic conditions in the Seoul autonomous mobility testbed”
    Quote in the paperNot a substantial claim
41 of 41

  • The paper relies on this without support.

    AVs with imperfect autonomous driving systems (ADS) are limited in their ability to improve traffic safety.

    “However, AVs with imperfect autonomous driving systems (ADS) are limited in their ability to improve traffic safety.”

  • The paper relies on this without support.

    The driving safety vulnerability of an AV in an ODD is determined by the characteristics of the ODD and the AV's driving behavior.

    “The driving safety vulnerability of an AV in an ODD is determined by the characteristics of the ODD and the AV's driving behavior.”

  • The paper relies on this without support.

    The VISSIM simulation environment calibrated with AVD and traffic data sufficiently reproduces real-world mixed traffic conditions for evaluating AV driving safety by ODD.

    “The analysis area was set as the Seoul autonomous mobility testbed.”
    “The driving behavior, road network, traffic volume, and vehicle types of AVs and MVs were implemented to establish the VISSIM environment.”

  • The paper relies on this without support.

    The AVD collected from five AVs are representative of AV driving behavior in the Seoul autonomous mobility testbed.

    “AVD were collected from a total of five AVs between 10 February 2022, and 31 October 2022, as presented in Table 1.”

  • The paper relies on this without support.

    The selected ODDs cover the relevant urban road network conditions for assessing AV driving safety.

    “A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses.”

  • The paper relies on this without support.

    The simulation environment replicates real-world AV operational characteristics.

    “The driving behavior of AVs was implemented in the simulation to replicate the operational characteristics of real-world AVs.”

  • The paper relies on this without support.

    The selected ODDs are representative of urban AV driving safety scenarios.

    “The study focused on the most common ODD scenarios to ensure general applicability and relevance, avoiding atypical or rare configurations.”

  • The paper relies on this without support.

    Bicycles travel at higher speeds compared to pedestrians and exhibit more erratic behavior than motor vehicles.

    “Bicycles travel at higher speeds compared to pedestrians and exhibit more erratic behavior than motor vehicles.”

  • The paper relies on this without support.

    The default VISSIM parameters for pedestrians and cyclists adequately represent their behavior.

    “The behavior of pedestrians and cyclists in the simulation was implemented using the default parameters provided by VISSIM for adult male and female walking speeds and bicycle parameters.”

  • The paper relies on this without support.

    The CoEXist parameters included in VISSIM adequately represent vehicle behavior where AVD-based implementation was not possible.

    “In addition, the CoEXist parameters included in VISSIM were used for parameters where it was not possible to implement vehicle behavior based on AVD.”
10 of 10

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Unsignalized-left-turn indicator differs between prose and table

    Major

    The prose identifies VF acceleration as a promising indicator for unsignalized left turns, whereas Table 4 lists VF jerk instead.

  • Abstract's highest-risk list omits a higher-scoring ODD

    Minor

    The abstract presents roundabouts at 0.501 among the highest-risk ODDs, but the reported intersection-crosswalk score is 0.552-higher than both roundabouts and signalized right turns.

  • Wang et al. citation points to a different reference

    Minor

    The discussion attributes a 2024 work to Wang et al. as reference [57], but [57] lists Ko et al. (2021). The Wang et al. (2024) work is listed as [58].

  • DRAC conflict threshold points in opposite directions

    Minor

    The methods text defines a DRAC conflict when deceleration exceeds 3.35 m/s2, but Table 3 defines it when DRAC is less than 3.35 m/s2.

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Jung, A.R., Jo, Y., Oh, C., Park, J.H., and Yun, D.G. (2023, January 8–12). Evaluating Driving Behavior of Mixed Car-Following Situations Consisting of Autonomous and Manual Vehicles by Road Alignment Conditions. Proceedings of the 102nd Annual Meeting of the Transportation Research Board, Washington, DC, USA.

Cited by

Sorted by citations

Related

Authors
Kim, H., Ko, J., Oh, C., & Kim, S.
Affiliations
  • Hanyang University (KR)
  • Gyeongsang National University (KR)
Corresponding author
Kim, Seoungbum
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2024-11-06
Volume
16
Issue
22
Pages
9672
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su16229672
OpenAlex
W4404089951
Semantic Scholar
3ba45dae764e5f0743c827a57c89fef8d3d143a8
Cited by
18
References
53
Citations by year
2026: 12 · 2025: 6
FWCI
2.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
Korea Institute of Police Technology (KIPoT) (RS-2023-00238253)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
autonomous vehicles, operational design domain, mixed traffic, signalized intersections, left turns, roundabouts, bus stops, bicycle lanes, pedestrian crossings, vehicle interaction, traffic simulation, risk scoring

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-23.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-23.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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