Personalizing driver safety interfaces via driver cognitive factors inference.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Aug 2024
05 Aug 2024
Historique:
received:
18
01
2024
accepted:
17
06
2024
medline:
6
8
2024
pubmed:
6
8
2024
entrez:
5
8
2024
Statut:
epublish
Résumé
Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver's latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.
Identifiants
pubmed: 39103366
doi: 10.1038/s41598-024-65144-8
pii: 10.1038/s41598-024-65144-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18058Informations de copyright
© 2024. Toyota Research Institute, Inc.
Références
Singh, S. Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Tech. Rep. DOT HS 812, 115 (2015).
Bareiss, M., Scanlon, J., Sherony, R. & Gabler, H. C. Crash and injury prevention estimates for intersection driver assistance systems in left turn across path/opposite direction crashes in the united states. Traffic Inj. Prev. 20, S133–S138 (2019).
pubmed: 31381453
doi: 10.1080/15389588.2019.1610945
Department of Transportation, U. S. NHTSA releases 2019 crash fatality data (2019).
Walshe, E. A., Ward McIntosh, C., Romer, D. & Winston, F. K. Executive function capacities, negative driving behavior and crashes in young drivers. Int. J. Environ. Res. Public Health 14, 1314 (2017).
pubmed: 29143762
pmcid: 5707953
doi: 10.3390/ijerph14111314
Albert, D., Chein, J. & Steinberg, L. The teenage brain: Peer influences on adolescent decision making. Curr. Dir. Psychol. Sci. 22, 114–120 (2013).
pubmed: 25544805
pmcid: 4276317
doi: 10.1177/0963721412471347
Barati, F., Pourshahbaz, A., Nosratabadi, M. & Mohammadi, Z. The role of impulsivity, attentional bias and decision-making styles in risky driving behaviors. Int. J. High Risk Behav. Addict. 9, 1-e98001 (2020).
doi: 10.5812/ijhrba.98001
Munakata, Y. et al. A unified framework for inhibitory control. Trends Cogn. Sci. 15, 453–459 (2011).
pubmed: 21889391
pmcid: 3189388
doi: 10.1016/j.tics.2011.07.011
Constantinou, E., Panayiotou, G., Konstantinou, N., Loutsiou-Ladd, A. & Kapardis, A. Risky and aggressive driving in young adults: Personality matters. Accid. Anal. Prev. 43, 1323–1331 (2011).
pubmed: 21545861
doi: 10.1016/j.aap.2011.02.002
Dahlen, E. R., Martin, R. C., Ragan, K. & Kuhlman, M. M. Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accid. Anal. Prev. 37, 341–348 (2005).
pubmed: 15667821
doi: 10.1016/j.aap.2004.10.006
Hayashi, Y., Foreman, A. M., Friedel, J. E. & Wirth, O. Executive function and dangerous driving behaviors in young drivers. Transp. Res. Part F Traffic Psychol. Behav. 52, 51–61 (2018).
pubmed: 31024220
pmcid: 6477690
doi: 10.1016/j.trf.2017.11.007
National Research Council et al.Preventing Teen Motor Crashes: Contributions from the Behavioral and Social Sciences: Workshop Report (National Academies Press, 2007).
Hatfield, J., Williamson, A., Kehoe, E. J. & Prabhakharan, P. An examination of the relationship between measures of impulsivity and risky simulated driving amongst young drivers. Accid. Anal. Prev. 103, 37–43 (2017).
pubmed: 28384487
doi: 10.1016/j.aap.2017.03.019
Jongen, E. M. M., Brijs, K., Komlos, M., Brijs, T. & Wets, G. Inhibitory control and reward predict risky driving in young novice drivers—a simulator study. Proced. Soc. Behav. Sci. 20, 604–612 (2011).
doi: 10.1016/j.sbspro.2011.08.067
Sârbescu, P. & Rusu, A. Personality predictors of speeding: Anger-aggression and impulsive-sensation seeking. A systematic review and meta-analysis. J. Safety Res. 77, 86–98 (2021).
pubmed: 34092331
doi: 10.1016/j.jsr.2021.02.004
Memarian, M., Lazuras, L., Rowe, R. & Karimipour, M. Impulsivity and self-regulation: A dual-process model of risky driving in young drivers in Iran. Accid. Anal. Prevent. 187, 107055 (2023).
doi: 10.1016/j.aap.2023.107055
Lazuras, L., Rowe, R., Poulter, D. R., Powell, P. A. & Ypsilanti, A. Impulsive and self-regulatory processes in risky driving among young people: A dual process model. Front. Psychol. 10, 439067 (2019).
doi: 10.3389/fpsyg.2019.01170
Ju, U., Williamson, J. & Wallraven, C. Predicting driving speed from psychological metrics in a virtual reality car driving simulation. Sci. Rep. 12, 10044 (2022).
pubmed: 35710859
pmcid: 9203461
doi: 10.1038/s41598-022-14409-1
McDonald, A., Carney, C. & McGehee, D. V. Vehicle owners’ experiences with and reactions to advanced driver assistance systems (2018).
Montgomery, J., Kusano, K. D. & Gabler, H. C. Age and gender differences in time to collision at braking from the 100-car naturalistic driving study. Traffic Inj. Prev. 15(Suppl 1), S15-20 (2014).
pubmed: 25307380
doi: 10.1080/15389588.2014.928703
Paaver, M. et al. Preventing risky driving: A novel and efficient brief intervention focusing on acknowledgement of personal risk factors. Accid. Anal. Prevent. 50, 430–437 (2013).
doi: 10.1016/j.aap.2012.05.019
Horberry, T., Regan, M. A. & Stevens, A. Driver Acceptance of New Technology: Theory, Measurement and Optimisation (Crc Press, 2018).
Af Wåhlberg, A., Dorn, L. & Kline, T. The manchester driver behaviour questionnaire as a predictor of road traffic accidents. Theor. Issues Ergon. Sci. 12, 66–86 (2011).
doi: 10.1080/14639220903023376
O’Brien, F. & Gormley, M. The contribution of inhibitory deficits to dangerous driving among young people. Accid. Anal. Prev. 51, 238–242 (2013).
pubmed: 23279959
doi: 10.1016/j.aap.2012.11.024
Chang, Z., Lichtenstein, P., D’Onofrio, B. M., Sjölander, A. & Larsson, H. Serious transport accidents in adults with attention-deficit/hyperactivity disorder and the effect of medication: A population-based study. JAMA Psychiat. 71, 319–325 (2014).
doi: 10.1001/jamapsychiatry.2013.4174
Gemming, L., Jiang, Y., Swinburn, B., Utter, J. & Mhurchu, C. N. Under-reporting remains a key limitation of self-reported dietary intake: An analysis of the 2008/09 New Zealand adult nutrition survey. Eur. J. Clin. Nutr. 68, 259–264 (2014).
pubmed: 24300904
doi: 10.1038/ejcn.2013.242
Dougherty, D. M., Mathias, C. W., Marsh, D. M. & Jagar, A. A. Laboratory behavioral measures of impulsivity. Behav. Res. Methods 37, 82–90 (2005).
pubmed: 16097347
doi: 10.3758/BF03206401
Lipszyc, J. & Schachar, R. Inhibitory control and psychopathology: A meta-analysis of studies using the stop signal task. J. Int. Neuropsychol. Soc. 16, 1064–1076 (2010).
pubmed: 20719043
doi: 10.1017/S1355617710000895
Maack, D. J. & Ebesutani, C. A re-examination of the BIS/BAS scales: Evidence for BIS and bas as unidimensional scales. Int. J. Methods Psychiatr. Res. 27, e1612 (2018).
pubmed: 29575375
pmcid: 6877112
doi: 10.1002/mpr.1612
Cyders, M. A., Littlefield, A. K., Coffey, S. & Karyadi, K. A. Examination of a short English version of the UPPS-P impulsive behavior scale. Addict. Behav. 39, 1372–1376 (2014).
pubmed: 24636739
pmcid: 4055534
doi: 10.1016/j.addbeh.2014.02.013
Kaplan, S., Guvensan, M. A., Yavuz, A. G. & Karalurt, Y. Driver behavior analysis for safe driving: A survey. IEEE Trans. Intell. Transp. Syst. 16, 3017–3032 (2015).
doi: 10.1109/TITS.2015.2462084
Schaff, C. & Walter, M. R. Residual policy learning for shared autonomy. In Robotics Science and Systems (2020). arXiv:2004.05097 .
Losey, D. P. et al. Learning latent actions to control assistive robots. Auton. Robots 46, 115–147 (2022).
pubmed: 34366568
doi: 10.1007/s10514-021-10005-w
Backman, K., Kulić, D. & Chung, H. Reinforcement learning for shared autonomy drone landings (2022). arXiv:2202.02927 .
Nidamanuri, J., Nibhanupudi, C., Assfalg, R. & Venkataraman, H. A progressive review: Emerging technologies for ADAS driven solutions. IEEE Trans. Intell. Veh. 7, 326–341 (2022).
doi: 10.1109/TIV.2021.3122898
Xie, A., Losey, D. P., Tolsma, R., Finn, C. & Sadigh, D. Learning latent representations to influence multi-agent interaction. In Conf. on Robot Learning (2020). arXiv:2011.06619 .
Tsividis, P. A. et al. Human-Level reinforcement learning through Theory-Based modeling, exploration, and planning. arXiv (2021). arXiv:2107.12544 .
Mazza, G. L. et al. Correlation database of 60 cross-disciplinary surveys and cognitive tasks assessing self-regulation. J. Pers. Assess. 103, 238–245 (2021).
pubmed: 32148088
doi: 10.1080/00223891.2020.1732994
Yang, R., Chen, J. & Narasimhan, K. Improving dialog systems for negotiation with personality modeling. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 681–693 (Association for Computational Linguistics, Online, 2021).
Song, K. et al. Recommendation vs sentiment analysis: A text-driven latent factor model for rating prediction with cold-start awareness. In Int. Joint Conf. on Artificial Intelligence, Research Collection School Of Computing and Information Systems, 2744 (AAAI Press, 2017).
Yu, Z., Lian, J., Mahmoody, A., Liu, G. & Xie, X. Adaptive user modeling with long and short-term preferences for personalized recommendation. In Int. Joint Conf. on Artificial Intelligence (California, 2019).
Tanjim, M. M. et al. Attentive sequential models of latent intent for next item recommendation. In Proceedings of The Web Conference 2020, WWW ’20, 2528–2534 (Association for Computing Machinery, New York, NY, USA, 2020).
Rudenko, A. et al. Human motion trajectory prediction: A survey. IJRR (2019).
Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9 (2008).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
pubmed: 9377276
doi: 10.1162/neco.1997.9.8.1735
Kingma, D. P. & Welling, M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
Gutmann, M. & Hyvarinen, A. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, 297–304.
Khosla, P. et al. Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661–18673 (2020).
Rai, N., Adeli, E., Lee, K.-H., Gaidon, A. & Niebles, J. C. Cocon: Cooperative-contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3384–3393 (2021).
Kingma, D. P. & Welling, M. Auto-Encoding variational bayes. In Int. Conf. on Learning Representations (2014).
Rezende, D. J., Mohamed, S. & Wierstra, D. Stochastic backpropagation and approximate inference in deep generative models. In Int. Conf. on Machine Learning (2014).
Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).
doi: 10.1145/1961189.1961199
Jonah, B. A. Age differences in risky driving. Health Educ. Res. 5, 139–149 (1990).
doi: 10.1093/her/5.2.139
Zhang, Y., Fu, C. & Hu, L. Yellow light dilemma zone researches: A review. J. Traffic Transp. Eng. (English Edition) 1, 338–352 (2014).
doi: 10.1016/S2095-7564(15)30280-4
Deo, N. & Trivedi, M. M. Multi-Modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In IVS (2018).
Best, A., Anderson, J. & Patrikalakis, A. Driver-in-the-loop simulation for guardian and chauffeur (2022).
Schrum, M. L., Sumner, E., Gombolay, M. C. & Best, A. Maveric: A data-driven approach to personalized autonomous driving. Trans. Rob. 40, 1952–1965. https://doi.org/10.1109/TRO.2024.3359543 (2024).
doi: 10.1109/TRO.2024.3359543
Karagulle, R., Ozay, N., Arechiga, N., DeCastro, J. & Best, A. Incorporating logic in online preference learning for safe personalization of autonomous vehicles. 1–11, https://doi.org/10.1145/3641513.3650129 (2024).
Motion Systems. 6 DOF Platform. https://motionsystems.eu/ (2023).
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A. & Koltun, V. Carla: An open urban driving simulator. In Conference on robot learning, 1–16 PMLR, 2017).
Carver, C. S. & White, T. L. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the bis/bas scales. J. Pers. Soc. Psychol. 67, 319 (1994).
doi: 10.1037/0022-3514.67.2.319
Whiteside, S. P., Lynam, D. R., Miller, J. D. & Reynolds, S. K. Validation of the UPPS impulsive behaviour scale: A four-factor model of impulsivity. Eur. J. Pers. 19, 559–574 (2005).
doi: 10.1002/per.556
Gomez, P., Ratcliff, R. & Perea, M. A model of the go/no-go task. J. Exp. Psychol. Gen. 136, 389 (2007).
pubmed: 17696690
pmcid: 2701630
doi: 10.1037/0096-3445.136.3.389
Lappin, J. S. & Eriksen, C. W. Use of a delayed signal to stop a visual reaction-time response. J. Exp. Psychol. 72, 805 (1966).
doi: 10.1037/h0021266
Verbruggen, F. et al. A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. Elife 8, e46323 (2019).
pubmed: 31033438
pmcid: 6533084
doi: 10.7554/eLife.46323
Team, J. Jasp (version 0.18.2)[computer software] (2024).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
doi: 10.18637/jss.v067.i01
Megías, A., Di Stasi, L. L., Maldonado, A., Catena, A. & Cándido, A. Emotion-laden stimuli influence our reactions to traffic lights. Transport. Res. F: Traffic Psychol. Behav. 22, 96–103 (2014).
doi: 10.1016/j.trf.2013.09.017
Woide, M., Miller, L., Colley, M., Damm, N. & Baumann, M. I’ve got the power: Exploring the impact of cooperative systems on driver-initiated takeovers and trust in automated vehicles. In Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 123–135 (2023).
Scally, K. et al. Impact of external cue validity on driving performance in Parkinson’s disease. Parkinsons Dis. 2011, 159621 (2011).
pubmed: 21789275
pmcid: 3140707
Zhang, Y. & Kumada, T. Automatic detection of mind wandering in a simulated driving task with behavioral measures. PLoS One 13, e0207092 (2018).
pubmed: 30419060
pmcid: 6231636
doi: 10.1371/journal.pone.0207092
Chein, J., Albert, D., O’Brien, L., Uckert, K. & Steinberg, L. Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Dev. Sci. 14, F1-10 (2011).
pubmed: 21499511
pmcid: 3075496
doi: 10.1111/j.1467-7687.2010.01035.x