Safety Research

Safety Research Papers

39 results found

• Comparison of Waymo Rider-Only Crash Rates by Crash Type to Human Benchmarks at 56.7 Million Miles

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  Kusano, K.D., Scanlon, J. M., Chen, Y., McMurry, T. L., Gode, T., Victor, T. (2025). Comparison of Waymo Rider-Only Crash Rates by Crash Type to Human Benchmarks at 56.7 Million Miles. Traffic Injury Prevention (In Press). https://doi.org/10.1080/15389588.2025.2499887

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• Being good (at driving): Characterizing behavioral expectations on automated and human driven vehicles

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  Fraade-Blanar, L., Favaro, F. M., Engstrom, J., Cefkin, M., Best R., Lee, J., Victor, T. (2025). Being good (at driving): Characterizing behavioral expectations on automated and human driven vehicles. Waymo LLC.

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• Do Autonomous Vehicles Outperform Latest-Generation Human-Driven Vehicles? A Comparison to Waymo's Auto Liability Insurance Claims at 25 Million Miles

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  Di Lillo, L., Gode, T., Zhou, X., Scanlon, J. M., Chen, R., & Victor, T. (2024). Do Autonomous Vehicles Outperform Latest-Generation Human-Driven Vehicles? A Comparison to Waymo’s Auto Liability Insurance Claims at 25.3M Miles.

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• Baseline vulnerable road user injury risk in multiple U.S. dense-urban driving environments

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  Campolettano, E. T., Scanlon, J. M., Kadar, I., Lavy, L. L., Moura, D., & Kusano, K. D. (2024). Baseline vulnerable road user injury risk in multiple U.S. dense-urban driving environments. Traffic Injury Prevention, 1-11. https://doi.org/10.1080/15389588.2024.2364050.

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• Kinematic characterization of micro-mobility vehicles during evasive maneuvers

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  Terranova, P., Liu, S.Y., Jain, S., Engstrom, J., Perez, M.A. (2024). Kinematic Characterization of Micro-Mobility Vehicles During Evasive Maneuvers. Journal of Safety Research, 91, 342-353. https://doi.org/10.1016/j.jsr.2024.09.020

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• Characterising vulnerable road user evasive manoeuvring in real-world crashes: Injury risk implications

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  Campolettano, E. T., Scanlon, J. M., Kusano, K. D. (2024). Characterising Vulnerable Road User Evasive Manoeuvring in Real-World Crashes: Injury Risk Implications. In Proceedings of the International Research Consortium on Biomechanics of Injury (IRCOBI) Conference 2024, IRC‐24‐117.

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• Active inference-based modeling of human driver collision avoidance behavior

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  Schumann, J. F., Engström, J., O’Kelly, M., Kober, J., and Zgonnikov, A. 2024. Active inference-based modeling of human driver collision avoidance behavior. Poster presented at the 5th International Workshop on Active Inference, 9-11 September 2024 in Oxford, UK.

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• Active inference as a general framework for modeling human driving behavior

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  Engström, J., O’Kelly, M., Johnson, L., Dinparastdjadid, A., Liu, S-Y and Messias, J., 2024. Active inference as a general framework for modeling human driving behavior. Poster presented at the 5th International Workshop on Active Inference, 9-11 September 2024 in Oxford, UK

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• Representative cyclist collision injury risk distributions for a dense-urban US ODD using naturalistic dash camera data

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  Campolettano, E. T., Scanlon, J. M., Kusano, K. D. (2024). Representative Cyclist Collision Injury Risk Distributions for a Dense-Urban US ODD Using Naturalistic Dash Camera Data. SAE Technical Paper No. 2024-01-2645. https://doi.org/10.4271/2024-01-2645

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• Dynamic Benchmarks: Spatial and Temporal Alignment for ADS Performance Evaluation

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  Chen, Y., Scanlon, J. M., Kusano, K. D., McMurry, T., Victor, T. (2024). Dynamic Benchmarks: Spatial and Temporal Alignment for ADS Performance Evaluation. arXiv preprint arXiv:2410.08903. https://arxiv.org/pdf/2410.08903

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• Comparative safety performance of autonomous and human drivers: A real-world case study of the Waymo One service

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  Di Lillo, L., Gode, T., Zhou, X., Atzei, M., Chen, R., & Victor, T. (2024). Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver. Heliyon, volume 10, issue 14, https://doi.org/10.1016/j.heliyon.2024.e34379

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• Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference

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  Engström, J., Wei, R., McDonald, A. D., Garcia, A., O’Kelly, M., & Johnson, L. (2024). Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference. Frontiers in Neurorobotics 18. https://doi.org/10.3389/fnbot.2024.1341750

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• Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework

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  Engström, J., Liu, S. Y., Dinparastdjadid, A., & Simoiu, C. (2024). Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework. Accident Analysis & Prevention, 198, 107460. https://doi.org/10.1016/j.aap.2024.107460

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• Comparison of Waymo rider-only crash data to human benchmarks at 7.1 million miles

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  Kusano, K. D., Scanlon, J. M., Chen, Y. H., McMurry, T. L., Chen, R., Gode, T., & Victor, T. (2024). Comparison of Waymo Rider-only crash data to human benchmarks at 7.1 million miles. Traffic Injury Prevention, 25(sup1), S66–S77. https://doi.org/10.1080/15389588.2024.2380786

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• RAVE checklist: Recommendations for overcoming challenges in retrospective studies of Automated Driving Systems

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  Scanlon, J. M., Teoh, E. R., Kidd, D. G., Kusano, K. D., Bärgman, J., Chi-Johnston, G., Di Lillo, L., Favaro, F., Flannagan, C., Liers, H., Lin, B., Lindman, M., McLaughlin, S., Perez, M., & Victor, T. (2024). RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems. Traffic Injury Prevention. 1–14. https://doi.org/10.1080/15389588.2024.2435620

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• Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

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  Scanlon, J. M., Kusano, K. D., Fraade-Blanar, L. A., McMurry, T. L., Chen, Y. H., & Victor, T. (2024). Benchmarks for Retrospective Automated Driving System Crash Rate Analysis Using Police-Reported Crash Data. Traffic Injury Prevention, 25(sup1), S51–S65. https://doi.org/10.1080/15389588.2024.2380522

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• Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data

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  Schubert, A., Campolettano, E. T., Scanlon, J. M., McMurry, T. L. & Unger, T. (2024). Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data. Traffic Injury Prevention, 25(sup1), S105–S115. https://doi.org/10.1080/15389588.2024.2400276

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• Passenger and heavy vehicle collisions with pedestrians: Assessment of injury mechanisms and risk

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  Schubert, A., Babisch, S., Scanlon, J. M., Campolettano, E. T., Roessler, R., Unger, T., McMurry, T. L. (2023). Passenger and heavy vehicle collisions with pedestrians: Assessment of injury mechanisms and risk. Accident Analysis & Prevention, 190, 107139. https://doi.org/10.1016/j.aap.2023.107139

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• Descriptive analysis of cyclist dooring events using data from the National Electronic Injury Surveillance System (NEISS)

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  Campolettano, E. T., Scanlon, J. M., Victor, T. (2023). Descriptive Analysis of Cyclist Dooring Events Using Data from the National Electronic Injury Surveillance System (NEISS). In Proceedings of the International Research Council On Biomechanics of Injury (IRCOBI) Conference 2023, Cambridge, UK. Paper number IRC-23-112.

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• Representative pedestrian collision injury risk distributions for a dense-urban US ODD using naturalistic dash camera data

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  Campolettano, E., Scanlon, J. M., Victor, T. (2023). Representative pedestrian collision injury risk distributions for a dense-urban US ODD using naturalistic dash camera data. In Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Yokohama, Japan. Paper number 23-0075.

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• Measuring surprise in the wild

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  Dinparastdjadid, A., Supeene, I., & Engström, J. (2023). Measuring surprise in the wild. arXiv preprint arXiv:2305.07733.
  https://doi.org/10.48550/arXiv.2305.07733

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• Building a credible case for safety: Waymo’s approach for the determination of absence of unreasonable Risk

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  Favaro, F., Fraade-Blanar, L., Schnelle, S., Victor, T., Peña, M., Engstrom, J., Scanlon, J., Kusano, K., Smith, D. (2023). Building a Credible Case for Safety: Waymo’s Approach for the Determination of Absence of Unreasonable Risk. arXiv preprint arXiv:2306.01917. https://doi.org/10.48550/arXiv.2306.01917

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• Interpreting safety outcomes: Waymo’s performance evaluation in the context of a broader determination of safety readiness

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  Favaro, F.M., Victor, T., Hohnhold, H. and Schnelle, S. (2023). Interpreting Safety Outcomes: Waymo’s Performance Evaluation in the Context of a Broader Determination of Safety Readiness. In 10th International Symposium on Transportation Data and Modelling (ISTDM2023) Ispra, 19-22 June 2023, Duboz, L. and Ciuffo, B. editor(s), Publications Office of the European Union, Luxembourg, 2023. https://doi.org/10.2760/135735.

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• Framework for a conflict typology including contributing factors for use In ADS safety evaluation

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  Kusano, K., Scanlon, J., Brännström, M., Engström, J., Victor, T. (2023). Framework for a conflict typology including causal factors for use in ADS safety evaluation. In Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Yokohama, Japan. Paper number 23-0328-O.

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• Challenges for the evaluation of automated driving systems using current ADAS and active safety test track protocols

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  Schnelle, S., Kusano, K., Favaro, F., Sier, G., Victor, T. (2023). Challenges for the evaluation of automated driving systems using current ADAS and active safety test track protocols. In Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Yokohama, Japan. Paper number 23-0329-O.

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• ADS standardization landscape: Making sense of its status and of the associated research questions

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  Schnelle, S., & Favaro, F. (2023). ADS standardization landscape: Making sense of its status and of the associated research questions. arXiv preprint arXiv:2306.17682. https://doi.org/10.48550/arXiv.2306.17682

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• Safety performance of the waymo rider-only automated driving system at one million miles

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  Victor, T., Kusano, K., Gode, T., Chen, R., & Schwall, M. (2023). Safety Performance of the Waymo Rider-Only Automated Driving System at One Million Miles.

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• World model learning from demonstrations with active inference: Application to driving behavior

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  Wei, R., Garcia, A., McDonald, A., Markkula, G., Engstrom, J., Supeene, I., O’Kelly, M. (2022). World Model Learning from Demonstrations with Active Inference: Application to Driving Behavior. In: Buckley, C.L., et al. Active Inference. IWAI 2022. Communications in Computer and Information Science, vol 1721. Springer, Cham. https://doi.org/10.1007/978-3-031-28719-0_9

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• An active inference model of car following: Advantages and applications

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  Wei, R., McDonald, A.D., Garcia, A., Markkula, G., Engstrom, J., O’Kelly, M. (2023). An active inference model of car following: Advantages and applications. arXiv preprint arXiv:2303.15201. https://doi.org/10.48550/arXiv.2303.15201

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• Determination of functional scenarios for intersection collisions

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  Bangert, L. G., Lubash, T., Scanlon, J. M., Kusano, K. D., & Riexinger, L. E. (2023). Determination of functional scenarios for intersection collisions. Accident Analysis & Prevention, 193, 107326. https://doi.org/10.1016/j.aap.2023.107326

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• Waymo's fatigue risk management framework: prevention, monitoring, and mitigation of fatigue-induced risks while testing automated driving systems

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  Favaro, F., Hutchings, K., Nemec, P., Cavalcante, L., Victor, T. (2022). Waymo’s fatigue risk management framework: prevention, monitoring, and mitigation of fatigue-induced risks while testing automated driving systems. arXiv preprint arXiv:2208.12833. https://doi.org/10.48550/arXiv.2208.12833

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• Methodology for determining maximum injury potential for automated driving system evaluation

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  Kusano, K., & Victor, T. (2022). Methodology for determining maximum injury potential for automated driving system evaluation. Traffic Injury Prevention, 23(sup1), S224–S227. https://doi.org/10.1080/15389588.2022.2125231

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• Collision avoidance testing of the Waymo automated driving system

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  Kusano, K., Beatty, K., Schnelle, S., Favaro, F., Crary, C., & Victor, T. 2022. Collision avoidance testing of the Waymo automated driving system. arXiv preprint arXiv:2212.08148. https://doi.org/10.48550/arXiv.2212.08148

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• Collision avoidance effectiveness of an automated driving system using a human driver behavior reference model in reconstructed fatal collisions

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  Scanlon, J.M., Kusano, K.D., Engström, J., Victor, T (2022). Collision avoidance effectiveness of an automated driving system using a human driver behavior reference model in reconstructed fatal collisions.

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• An omni-directional model of injury risk in planar crashes with application for autonomous vehicles

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  McMurry, T. L., Cormier, J. M., Daniel, T., Scanlon, J. M., & Crandall, J. R. (2021). An omni-directional model of injury risk in planar crashes with application for autonomous vehicles. Traffic Injury Prevention, 22(sup1), S122–S127. https://doi.org/10.1080/15389588.2021.1955108

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• Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain

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  Scanlon, J.M., Kusano, K.D., Daniel, T., Alderson, C., Ogle, A., Victor, T. (2021). Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain. Accident Analysis & Prevention, 163, 106454. https://doi.org/10.1016/j.aap.2021.106454

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• Waymo safety report

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  Waymo. (2021). Waymo Safety Report.

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• Waymo public road safety performance data

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  Schwall, M., Daniel, T., Victor, T., Favaro, F., Hohnhold, H. (2020). Waymo public road safety performance data. arXiv preprint arXiv:2011.00038. https://doi.org/10.48550/arXiv:2011.00038

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• Waymo's safety methodologies and safety readiness determinations

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  Webb, N., Smith, D., Ludwick, C., Victor, T., Hommes, Q., Favaro, F., Ivanov, G., Daniel, T. (2020). Waymo’s safety methodologies and safety readiness determinations. arXiv preprint arXiv:2011.00054. https://doi.org/10.48550/2011.00054

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