Análise de Métricas Telemáticas para Auditoria de Veículos Autônomos
Resumo
Veículos autônomos (AVs) usam sistemas de sensoriamento externo para obter informações sobre os diversos objetos presentes ao seu redor e assim tomar decisões de condução de forma automatizada. De fato, as informações obtidas através da telemática veicular servem para monitorar o funcionamento e a forma de condução de quaisquer veículos automotores, não apenas dos AVs. De acordo com a literatura, diferentes métricas são usadas por aplicativos de asseguradoras para avaliar situações de risco potencial e como os motoristas dirigem. Quanto a AVs, porém, a avaliação destas métricas ainda é incipiente, dado que a maioria destes veículos está em desenvolvimento. Assim, com o objetivo de avaliar e quantificar a gravidade de eventos de risco no trânsito envolvendo AVs e outros usuários viários, este trabalho estuda métricas de segurança substitutas baseado em leituras de sensores exteroceptivos e proprioceptivos contidas em conjuntos de dados de AVs. Os resultados indicam que ao menos 10% dos eventos representam algum nível risco para o AV. Estes resultados ajudam a explicar o comportamento e a tomada de decisões no sistema autônomo.
Referências
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Bellet, T., Cunneen, M., Mullins, M., Murphy, F., Pütz, F., Spickermann, F., Braendle, C. e Baumann, M. F. (2019). From semi to fully autonomous vehicles: New emerging risks and ethico-legal challenges for human-machine interactions. Transp. Res. Part F: Traffic Psychol. Behav., 63:153–164.
Betz, J., Heilmeier, A., Wischnewski, A., Stahl, T. e Lienkamp, M. (2019). Autonomous driving-a crash explained in detail. Appl. Sci., 9(23).
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G. e Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. Em IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), p. 11618–11628.
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Hydén, C. (1996). Traffic conicts technique: state-of-the-art. Traffic Safety Work with Video Processing, 37:3–14.
Hydén, C. (1987). The Development of a Method for Trafc Safety Evaluation: The Swedish Trafc Conicts Technique. PhD thesis, Lund University, Sweeden.
Johnsson, C., Laureshyn, A. e Ceunynck, T. D. (2018). In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators. Transp. Rev., 38(6):765–785.
Kilicarslan, M. e Zheng, J. Y. (2019). Predict vehicle collision by TTC from motion using a single video camera. IEEE Trans. Intell. Transp. Syst., 20(2):522–533.
Li, Y., Lu, J. e Xu, K. (2017). Crash risk prediction model of lane-change behavior on approaching intersections. Discrete Dyn. Nat. Soc., 2017.
Nodine, E., Stevens, S., Lam, A., Jackson, C., Najm, W. G. et al. (2015). Independent evaluation of light-vehicle safety applications based on vehicle-to-vehicle communications used in the 2012-2013 safety pilot model deployment. Relatório técnico, NHTSA.
Ortiz, F. M., Sammarco, M., Costa, L. H. M. K. e Detyniecki, M. (2020). Vehicle telematics via exteroceptive sensors: A survey. arXiv:2008.12632.
SAE (2018). SAE standard J3016: Taxonomy and definitions for terms related to on-road motor vehicles. Relatório técnico, Society of Automotive Engineers (SAE).
Tarko, A., Davis, G., Saunier, N. e Sayed, T. (2009). Surrogate measures of safety. Safe Mobility: Challenges, Method. Solutions, 11:383–405.
Virdi, N., Grzybowska, H., Waller, S. T. e Dixit, V. (2019). A safety assessment of mixed eets with connected and autonomous vehicles using the surrogate safety assessment module. Accid. Anal. Prev., 131:95–111.
Xie, K., Yang, D., Ozbay, K. e Yang, H. (2019). Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure. Accid. Anal. Prev., 125:311–319.
Zhang, J., Wu, K., Cheng, M., Yang, M., Cheng, Y. e Li, S. (2020). Safety evaluation for connected and autonomous vehicles’ exclusive lanes considering penetrate ratios and impact of trucks using surrogate safety measures. J. Adv. Transp., 2020:1–16.
Allen, B. L., Shin, B. e Cooper, P. J. (1978). Analysis of traffic conicts and collisions. Transp. Res. Rec.
Almqvist, S., Hydén, C. e Risser, R. (1991). Use of speed limiters in cars for increased safety and a better environment. Transp. Res. Rec.
Aycard, O., Baig, Q., Bota, S., Nashashibi, F., Nedevschi, S., Pantilie, C., Parent, M., Resende, P. e Vu, T. (2011). Intersection safety using lidar and stereo vision sensors. Em IEEE Intell. Veh. Symp. (IV), p. 863–869.
Bellet, T., Cunneen, M., Mullins, M., Murphy, F., Pütz, F., Spickermann, F., Braendle, C. e Baumann, M. F. (2019). From semi to fully autonomous vehicles: New emerging risks and ethico-legal challenges for human-machine interactions. Transp. Res. Part F: Traffic Psychol. Behav., 63:153–164.
Betz, J., Heilmeier, A., Wischnewski, A., Stahl, T. e Lienkamp, M. (2019). Autonomous driving-a crash explained in detail. Appl. Sci., 9(23).
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G. e Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. Em IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), p. 11618–11628.
Campbell, K. (2012). The SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety. Transp. Res. Board.
Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S. E., Sudweeks, J., Perez, M. A., Hankey, J., Ramsey, D., Gupta, S. et al. (2006). The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment. Relatório técnico, NHTSA.
Hayward, J. C. (1972). Near-miss determination through use of a scale of danger. Highway Res. Rec.
He, Z., Qin, X., Liu, P. e Sayed, M. A. (2018). Assessing surrogate safety measures using a safety pilot model deployment dataset. Transp. Res. Rec., 2672(38):1–11.
Hydén, C. (1996). Traffic conicts technique: state-of-the-art. Traffic Safety Work with Video Processing, 37:3–14.
Hydén, C. (1987). The Development of a Method for Trafc Safety Evaluation: The Swedish Trafc Conicts Technique. PhD thesis, Lund University, Sweeden.
Johnsson, C., Laureshyn, A. e Ceunynck, T. D. (2018). In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators. Transp. Rev., 38(6):765–785.
Kilicarslan, M. e Zheng, J. Y. (2019). Predict vehicle collision by TTC from motion using a single video camera. IEEE Trans. Intell. Transp. Syst., 20(2):522–533.
Li, Y., Lu, J. e Xu, K. (2017). Crash risk prediction model of lane-change behavior on approaching intersections. Discrete Dyn. Nat. Soc., 2017.
Nodine, E., Stevens, S., Lam, A., Jackson, C., Najm, W. G. et al. (2015). Independent evaluation of light-vehicle safety applications based on vehicle-to-vehicle communications used in the 2012-2013 safety pilot model deployment. Relatório técnico, NHTSA.
Ortiz, F. M., Sammarco, M., Costa, L. H. M. K. e Detyniecki, M. (2020). Vehicle telematics via exteroceptive sensors: A survey. arXiv:2008.12632.
SAE (2018). SAE standard J3016: Taxonomy and definitions for terms related to on-road motor vehicles. Relatório técnico, Society of Automotive Engineers (SAE).
Tarko, A., Davis, G., Saunier, N. e Sayed, T. (2009). Surrogate measures of safety. Safe Mobility: Challenges, Method. Solutions, 11:383–405.
Virdi, N., Grzybowska, H., Waller, S. T. e Dixit, V. (2019). A safety assessment of mixed eets with connected and autonomous vehicles using the surrogate safety assessment module. Accid. Anal. Prev., 131:95–111.
Xie, K., Yang, D., Ozbay, K. e Yang, H. (2019). Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure. Accid. Anal. Prev., 125:311–319.
Zhang, J., Wu, K., Cheng, M., Yang, M., Cheng, Y. e Li, S. (2020). Safety evaluation for connected and autonomous vehicles’ exclusive lanes considering penetrate ratios and impact of trucks using surrogate safety measures. J. Adv. Transp., 2020:1–16.
Publicado
16/08/2021
Como Citar
ORTIZ, Fernando M.; SAMMARCO, Matteo; DETYNIECKI, Marcin; COSTA, Luís Henrique M. K..
Análise de Métricas Telemáticas para Auditoria de Veículos Autônomos. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 15-28.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17101.
