PSI - Issue 24

Dario Vangi et al. / Procedia Structural Integrity 24 (2019) 423–436

435

D. Vangi et al. / Structural Integrity Procedia 00 (2019) 000–000

13

The present work evidences the benefit deriving from the use of the proposed criteria and how their employment by ADAS devices can be further optimized. Currently, as a priority, the intervention minimizes IR for the vehicle on which the ADAS is implemented: minimization of IR for the opponent vehicle is also possible, as well as the average IR between the two vehicles. Including IR for the opponent among the intervention criteria, their application field could be considerably widened, considering that IR curves for di ff erent types of vehicles (e.g., motorcycles) or vulnerable road users can be found in the literature. Furtherly, the discussion can also be expanded to IR curves associated with maximum abbreviated injury scale lower than 3 (e.g., MAIS 2 + ), hence including lower degrees of injury relative to the ones considered in the present work: this would allow limiting the number of serious injuries and also moderate injuries, enabling the amplification of the overall e ff ects on road safety. Bifulco, G.N., Pariota, L., Brackstione, M., Mcdonald, M., 2013. Driving behaviour models enabling the simulation of advanced driving assistance systems: revisiting the action point paradigm. Transport Res Part C: Emerg Technol 36, 352–366. Brach, R., Brach, M., 2011. The tire-force ellipse (friction ellipse) and tire characteristics. SAE paper 01-0094 1–10. Da´vid, B., La´ncz, G., Hunyady, G., 2019. Real-time behaviour planning and highway situation analysis concept with scenario classification and risk estimation for autonomous vehicles. Designs 3, 4. European Automotive Manufacturers Association, 2019. Average vehicle age. URL: https://www.acea.be/statistics/tag/category/ average-vehicle-age . European Commision, 2011. Roadmap to a single european transport area - towards a competitive and resource e ffi cient transport system Brussels. European Commision, 2017. Tra ffi c safety basic facts on junctions Directorate Geneal for Transport - Brussels. Gabauer, D.J., Gabler, H.C., 2008. Comparison of roadside crash injury metrics using event data recorders. Accid Anal Prev 40, 548–558. Gennarelli, T.A., Wodzin, E., 2008. Abbreviated injury scale 2005: update 2008. Barrington: Association for the Advancement of Automotive Medicine. Gietelink, O., Ploeg, J., De Schutter, B., Verhaegen, M., 2006. Development of advanced driver assistance systems with vehicle hardware-in-the loop simulations. Veh Syst Dyn 44, 569–590. Gunnarsson, J., Svensson, L., Danielsson, L., Bengtsson, F., 2007. Tracking vehicles using radar detections, in: 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 13-15 June 2007. pp. 296–302. Hakuli, S., Krug, M., 2016. Virtual Integration in the Development Process of ADAS. In: Handbook of Driver Assistance Systems. I ed. Switzerland: Springer. Han, I., 2018. Analysis of vehicle collision accidents based on qualitative mechanics. Forensic Sci Int 291, 53–61. Jurewicz, C., Sobhani, A., Woolley, J., Dutschke, J., Corben, B., 2016. Exploration of vehicle impact speed–injury severity relationships for application in safer road design. Transportation research procedia 14, 4247–4256. Kaempchen, N., Schiele, B., Dietmayer, K., 2009. Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios. IEEE Trans Intell Transp Syst 10, 678–687. Kolk, H., Tomasch, E., Sinz, W., Bakker, J., Dobberstein, J., 2016. Evaluation of a momentum based impact model and application in an e ff ectivity study considering junction accidents, in: International Conference on ESAR Expert Symposium on Accident Research, Hannover, Germany, 9-10 June 2016. pp. 1–12. Kullgren, A., 2008. Dose-response models and EDR data for assessment of injury risk and e ff ectiveness of safety systems, in: Proc of Int IRCOBI Conf, Bern, Switzerland, 17-19 September 2008. pp. 3–14. Le Guennec, Y., Brunet, J.P., Daim, F.Z., Chau, M., Tourbier, Y., 2018. A parametric and non-intrusive reduced order model of car crash simulation. Comput Methods Appl Mech Eng 338, 186–207. Liu, S., Huang, Y., Zhang, R., 2014. Obstacle recognition for ADAS using stereovision and snake models, in: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8-11 October 2014. pp. 99–104. Rieken, J., Maurer, M., 2016. Sensor scan timing compensation in environment models for automated road vehicles, in: 2016 IEEE 19th Interna tional Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1-4 November 2016. pp. 635–642. Scanlon, J.M., Kusano, K.D., Gabler, H.C., 2015. Analysis of driver evasive maneuvering prior to intersection crashes using event data recorders. Tra ffi c Inj Prev 16, S182–S189. Society of Automotive Engineers, 2018. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles Warrendale, PA. Spicer, R., Vahabaghaie, A., Bahouth, G., Drees, L., Martinez von Bu¨ low, R., Baur, P., 2018. Field e ff ectiveness evaluation of advanced driver assistance systems. Tra ffi c Inj Prev 19, S91–S95. Vangi, D., Begani, F., 2013. Energy loss in vehicle collisions from permanent deformation: an extension of the ‘triangle method’. Veh Syst Dyn 51, 857–876. Vangi, D., Begani, F., Spitzhu¨ ttl, F., Gulino, M.S., 2019b. Vehicle accident reconstruction by a reduced order impact model. Forensic Sci Int 298, 426.e1–426.e11. Vangi, D., Cialdai, C., Gulino, M.S., 2019c. Vehicle sti ff ness assessment for energy loss evaluation in vehicle impacts. Forensic Sci Int 300, 136–144. Vangi, D., Gulino, M.S., Cialdai, C., 2019a. Coherence assessment of accident database kinematic data. Accid Anal Prev 123, 356–364. References