{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:48:47Z","timestamp":1760230127468,"version":"build-2065373602"},"reference-count":90,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>The on-board sensors of connected autonomous vehicles (CAVs) are limited by their range and inability to see around corners or blind spots, otherwise known as non-line of sight scenarios (NLOS). These scenarios have the potential to be fatal (critical scenarios) as the sensors may detect an obstacle much later than the amount of time needed for the car to react. In such cases, mechanisms such as vehicular communication are required to extend the visibility range of the CAV. Despite there being a substantial body of work on the development of navigational and communication algorithms for such scenarios, there is no standard method for generating and selecting critical NLOS scenarios for testing these algorithms in a scenario-based simulation environment. This paper puts forward a novel method utilising a genetic algorithm for the selection of critical NLOS scenarios from the set of all possible NLOS scenarios in a particular road environment. The need to select critical scenarios is pertinent as the number of all possible driving scenarios generated is large and testing them against each other is time consuming, unnecessary and expensive. The selected critical scenarios are then validated for criticality by using a series of MATLAB based simulations.<\/jats:p>","DOI":"10.3390\/software1030011","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:06:00Z","timestamp":1657749960000},"page":"244-264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing"],"prefix":"10.3390","volume":"1","author":[{"given":"Tanvir","family":"Allidina","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence, Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8986-884X","authenticated-orcid":false,"given":"Lipika","family":"Deka","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2838-060X","authenticated-orcid":false,"given":"Daniel","family":"Paluszczyszyn","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"},{"name":"HORIBA MIRA Ltd., Watling Street, Nuneaton CV10 0TU, UK"}]},{"given":"David","family":"Elizondo","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.trc.2018.02.012","article-title":"Autonomous vehicle perception: The technology of today and tomorrow","volume":"89","author":"Gruyer","year":"2018","journal-title":"Transp. Res. Part C"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/ars-3-205-2005","article-title":"Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions","volume":"3","author":"Rasshofer","year":"2005","journal-title":"Adv. Radio Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mohammed, A.S., Amamou, A., Ayevide, F.K., Kelouwani, S., Agbossou, K., and Zioui, N. (2020). The perception system of intelligent ground vehicles in all weather conditions: A systematic literature review. Sensors, 20.","DOI":"10.3390\/s20226532"},{"key":"ref_4","unstructured":"Leibe, B., Seemann, E., and Schiele, B. (2005, January 20\u201325). Pedestrian detection in crowded scenes. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., and Z\u00f6llner, J.M. (2021, January 7). Description of Corner Cases in Automated Driving: Goals and Challenges. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00119"},{"key":"ref_6","first-page":"109","article-title":"Recent advances in connected and automated vehicles","volume":"6","author":"Elliott","year":"2019","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106447","DOI":"10.1016\/j.aap.2021.106447","article-title":"A context-aware driver model for determining recommended speed in blind intersection situations","volume":"163","author":"Saito","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_8","unstructured":"Hussein, M., Erol-Kantarci, M., and Sorour, S. (2020). Connected and Autonomous Vehicles in Smart Cities, CRC Press."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Payen de La Garanderie, G., Atapour Abarghouei, A., and Breckon, T.P. (2018, January 8\u201314). Eliminating the blind spot: Adapting 3D object detection and monocular depth estimation to 360\u00b0 Panoramic Imagery. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_48"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kwon, D., Malaiya, R., Yoon, G., Ryu, J.-T., and Pi, S.-Y. (2018). A Study on Development of the Camera-Based Blind Spot Detection System Using the Deep Learning Methodology. Appl. Sci., 9.","DOI":"10.3390\/app9142941"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCE.2018.2828440","article-title":"Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles","volume":"7","author":"Kukkala","year":"2018","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-642-03991-1_1","article-title":"Autonomous driving in Urban environments: Boss and the Urban Challenge","volume":"Volume 56","author":"Urmson","year":"2009","journal-title":"The DARPA Urban Challenge"},{"key":"ref_13","unstructured":"Chu, K. (2017). Methods and Systems for Blind Spot Detection in an Autonomous Vehicle. (20190011913A1), U.S. Patents, Available online: https:\/\/patents.google.com\/patent\/US20190011913A1\/en."},{"key":"ref_14","first-page":"1","article-title":"Towards a definition of the {Internet of Things (IoT)}","volume":"1","author":"HMinn","year":"2015","journal-title":"IEEE Internet Initiat."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Anaya, J.J., Talavera, E., Gimenez, D., Gomez, N., Felipe, J., and Naranjo, J.E. (2015, January 15\u201318). Vulnerable Road Users Detection Using V2X Communications. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.26"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MCOM.2015.7355568","article-title":"Enhancements of V2X communication in support of cooperative autonomous driving","volume":"53","author":"Hobert","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hussein, A., Mar\u00edn-Plaza, P., Garc\u00eda, F., and Armingol, J.M. (2019, January 16\u201319). Autonomous cooperative driving using V2X communications in off-road environment. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317790"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/15472450.2016.1247703","article-title":"Application of vehicle to another entity (V2X) communications for motorcycle crash avoidance","volume":"21","author":"Naranjo","year":"2016","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_19","first-page":"72","article-title":"Cooperative volunteer protocol to detect non-line of sight nodes in vehicular ad hoc networks","volume":"9","author":"Alodadi","year":"2017","journal-title":"Veh. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Menzel, T., Bagschik, G., and Maurer, M. (2018, January 26\u201330). Scenarios for Development, Test and Validation of Automated Vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, Changshu, China.","DOI":"10.1109\/IVS.2018.8500406"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, J., Yuan, B., and Tomizuka, M. (2019, January 27\u201330). Model-free Deep Reinforcement Learning for Urban Autonomous Driving. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917306"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"S52","DOI":"10.1080\/15389588.2019.1624732","article-title":"Functional decomposition\u2014A contribution to overcome the parameter space explosion during validation of highly automated driving","volume":"20","author":"Amersbach","year":"2019","journal-title":"Traffic Inj. Prev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Riedmaier, S., Schneider, D., Watzenig, D., Diermeyer, F., and Schick, B. (2021). Model validation and scenario selection for virtual-based homologation of automated vehicles. Appl. Sci., 11.","DOI":"10.3390\/app11010035"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100029","DOI":"10.1016\/j.treng.2020.100029","article-title":"Overtaking maneuver scenario building for autonomous vehicles with PreScan software","volume":"2","author":"Ortega","year":"2020","journal-title":"Transp. Eng."},{"key":"ref_25","unstructured":"Wongpiromsarn, T., and Murray, R.M. (2008, January 9\u201311). Formal Verification of an Autonomous Vehicle System. Proceedings of the Conference on Decision and Control 2008, Canc\u00fan, Mexico."},{"key":"ref_26","unstructured":"Ireland, M.L., Hoffmann, R., Miller, A., Norman, G., and Veres, S.M. (2016). A Continuous-Time Model of an Autonomous Aerial Vehicle to Inform and Validate Formal Verification Methods. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1145\/2507771.2507783","article-title":"Formal verification of phase-locked loops using reachability analysis and continuization","volume":"56","author":"Althoff","year":"2013","journal-title":"Commun. ACM"},{"key":"ref_28","unstructured":"Shalev-Shwartz, S., Shammah, S., and Shashua, A. (2017). On a Formal Model of Safe and Scalable Self-driving Cars. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"87456","DOI":"10.1109\/ACCESS.2020.2993730","article-title":"Survey on Scenario-Based Safety Assessment of Automated Vehicles","volume":"8","author":"Riedmaier","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1515\/auto-2017-0113","article-title":"Overview of HAD validation and passive HAD as a concept for validating highly automated cars","volume":"66","author":"Koenig","year":"2018","journal-title":"Automatisierungstechnik"},{"key":"ref_31","first-page":"60","article-title":"Virtual Assessment of Automation in Field Operation. A New Runtime Validation Method","volume":"10","author":"Wachenfeld","year":"2015","journal-title":"Rob. Auton. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21956","DOI":"10.1109\/ACCESS.2018.2828260","article-title":"Architecture design and implementation of an autonomous vehicle","volume":"6","author":"Zong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"(2021, December 04). Waymo, Safety Report and Whitepapers\u2014Waymo. Available online: https:\/\/waymo.com\/safety\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1016\/j.trpro.2016.05.234","article-title":"Methodology for Field Operational Tests of Automated Vehicles","volume":"14","author":"Barnard","year":"2016","journal-title":"Transp. Res. Procedia"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MITS.2014.2306552","article-title":"Making Bertha Drive\u2014An Autonomous Journey on a Historic Route","volume":"6","author":"Ziegler","year":"2014","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TRO.2014.2312453","article-title":"Online verification of automated road vehicles using reachability analysis","volume":"30","author":"Althoff","year":"2014","journal-title":"IEEE Trans. Robot."},{"key":"ref_37","unstructured":"(2021, December 04). Waymo, Waymo Opens Robo-Taxi Service to the Public in US City of Phoenix | Technology News. Available online: https:\/\/gadgets.ndtv.com\/transportation\/news\/google-waymo-self-driving-cars-phoenix-launch-2307438."},{"key":"ref_38","unstructured":"(2021, December 04). Tesla, Tesla\u2019s \u2018Dojo\u2019 Deployment in 2021 Will Make Autopilot and FSD a \u2018Distant First\u2019 against Rivals. Available online: https:\/\/www.teslarati.com\/tesla-dojo-autopilot-fsd-improvements-release-date\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bach, J., Holz\u00e4pfel, M., Otten, S., and Sax, E. (2017). Reactive-Replay Approach for Verification and Validation of Closed-Loop Control Systems in Early Development. SAE Technol. Pap.","DOI":"10.4271\/2017-01-1671"},{"key":"ref_40","unstructured":"Nilsson, J. (2014). Computational Verification Methods for Automotive Safety Systems, Chalmers Tekniska Hogskola."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kruber, F., Wurst, J., and Botsch, M. (2018, January 4\u20137). An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569682"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bolte, J.A., Bar, A., Lipinski, D., and Fingscheidt, T. (2019, January 9\u201312). Towards corner case detection for autonomous driving. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813817"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.aap.2014.08.012","article-title":"Typical pedestrian accident scenarios for the development of autonomous emergency braking test protocols","volume":"73","author":"Lenard","year":"2014","journal-title":"Accid. Anal. Prev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tuncali, C.E., Pavlic, T.P., and Fainekos, G. (2016, January 1\u20134). Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795751"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tuncali, C.E., Fainekos, G., Ito, H., and Kapinski, J. (2018, January 26\u201330). Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500421"},{"key":"ref_46","first-page":"1","article-title":"Scenario Based Testing of Automated Driving Systems: A Literature Survey","volume":"30","author":"Nalic","year":"2020","journal-title":"Proc. FISITA Web Congr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.tra.2016.09.010","article-title":"Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?","volume":"94","author":"Kalra","year":"2016","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ma, J., Schwarz, C., Wang, Z., Elli, M., Ros, G., and Feng, Y. (2019, January 15). New simulation tools for training and testing automated vehicles. Proceedings of the Automated Vehicles Symposium 2019, Orlando, FL, USA.","DOI":"10.1007\/978-3-030-52840-9_11"},{"key":"ref_49","unstructured":"(2021, April 07). Waymo. Available online: https:\/\/waymo.com\/."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"S65","DOI":"10.1080\/15389588.2019.1630827","article-title":"Traffic Injury Prevention A framework for definition of logical scenarios for safety assurance of automated driving A framework for definition of logical scenarios for safety assurance of automated driving","volume":"20","author":"Weber","year":"2019","journal-title":"Traffic Inj. Prev."},{"key":"ref_51","unstructured":"Nitsche, P. (2018). Safety-Critical Scenarios and Virtual Testing Procedures for Automated Cars at Road Intersections. [Ph.D. Thesis, Loughborough University]."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.iatssr.2017.02.001","article-title":"Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs","volume":"41","author":"Mahmud","year":"2017","journal-title":"IATSS Res."},{"key":"ref_53","unstructured":"Hayward, J.C. (1972). Near-Miss Determination through use of a scale of danger. Highw. Res. Board, 24\u201335. Available online: https:\/\/onlinepubs.trb.org\/Onlinepubs\/hrr\/1972\/384\/384-004.pdf."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Junietz, P., Bonakdar, F., Klamann, B., and Winner, H. (2018, January 4\u20137). Criticality Metric for the Safety Validation of Automated Driving using Model Predictive Trajectory Optimization. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems, Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569326"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/TITS.2016.2522507","article-title":"An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments","volume":"17","author":"Matthias","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0001-4575(00)00019-1","article-title":"Extended time-to-collision measures for road traffic safety assessment","volume":"33","author":"Minderhoud","year":"2001","journal-title":"Accid. Anal. Prev."},{"key":"ref_57","first-page":"14","article-title":"Application of Simulation-Based Traffic Conflict Analysis for Highway Safety Evaluation","volume":"12","author":"Yang","year":"2010","journal-title":"Sel. Proc. World Conf. Transp. Res."},{"key":"ref_58","unstructured":"Yang, H. (2012). Simulation-Based Evaluation of Traffic Safety Performance Using Surrogate Safety Measures, Rutgers The State University of New Jersey-New Brunswick."},{"key":"ref_59","first-page":"159","article-title":"Minimum and Comfortable Driving Headways: Reality versus Perception","volume":"43","author":"Shinar","year":"2016","journal-title":"Hum. Factors"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S0001-4575(02)00022-2","article-title":"A comparison of headway and time to collision as safety indicators","volume":"35","author":"Vogel","year":"2003","journal-title":"Accid. Anal. Prev."},{"key":"ref_61","unstructured":"Shbeeb, L. (2021, September 01). Development of Traffic Conflicts Technique for Different Environments: A Comparative Study of Pedestrian Conflicts in Sweden and Jordan. Available online: https:\/\/lup.lub.lu.se\/search\/publication\/19830."},{"key":"ref_62","unstructured":"Archer, J., and Kungl, T. (2005). Indicators for Traffic Safety Assessment and Prediction and Their Application in Micro-Simulation Modelling: A Study of Urban and Suburban Intersections. [Ph.D. Dissertaion, KTH Royal Institute of Technology]."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1177\/0361198106195300111","article-title":"Practical Method for Estimating Frequency of Right-Angle Collisions at Traffic Signals","volume":"1953","author":"Songchitruksa","year":"2006","journal-title":"Transp. Res. Rec."},{"key":"ref_64","unstructured":"Cunto, F. (2021, September 01). Assessing Safety Performance of Transportation Systems using Microscopic Simulation. Ph.D. Dissertation. Available online: http:\/\/hdl.handle.net\/10012\/4111."},{"key":"ref_65","first-page":"294","article-title":"A general formulation for time-to-collision safety indicator","volume":"166","author":"Saffarzadeh","year":"2013","journal-title":"Proc. Inst. Civ. Eng. Transp."},{"key":"ref_66","unstructured":"Kochenderfer, M.J., and Wheeler, T.A. (2019). Algorithms for Optimization, The MIT Press Cambridge."},{"key":"ref_67","first-page":"647234","article-title":"Optimization on Black Box Function Optimization Problem","volume":"2015","author":"Li","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kluck, F., Zimmermann, M., Wotawa, F., and Nica, M. (2019, January 22\u201326). Genetic Algorithm-Based Test Parameter Optimization for ADAS System Testing. Proceedings of the 19th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2019, Sofia, Bulgaria.","DOI":"10.1109\/QRS.2019.00058"},{"key":"ref_69","unstructured":"Abdessalem, R.B., Nejati, S., Briand, L.C., and Stifter, T. (June, January 27). Testing vision-based control systems using learnable evolutionary algorithms. Proceedings of the International Conference on Software Engineering, Gothenburg, Sweden."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cutrone, S., Liew, C.W., Utter, B., and Brown, A. (2018, January 7\u201310). A Framework for Identifying and Simulating Worst-Case Animal-Vehicle Interactions. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00344"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Gladisch, C., Heinz, T., Heinzemann, C., Oehlerking, J., von Vietinghoff, A., and Pfitzer, T. (2019, January 10\u201315). Experience Paper: Search-Based Testing in Automated Driving Control Applications. Proceedings of the 2019 34th IEEE\/ACM International Conference on Automated Software Engineering, San Diego, CA, USA.","DOI":"10.1109\/ASE.2019.00013"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gangopadhyay, B., Khastgir, S., Dey, S., Dasgupta, P., Montana, G., and Jennings, P. (2019, January 27\u201330). Identification of Test Cases for Automated Driving Systems Using Bayesian Optimization. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917103"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1049\/iet-its.2018.5335","article-title":"Rule-based searching for collision test cases of autonomous vehicles simulation","volume":"12","author":"Masuda","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lam, H., and Zhao, D. (2017, January 3\u20136). Sequential experimentation to efficiently test automated vehicles. Proceedings of the IEEE Winter Simulation Conference (WSC), Las Vegas, NV, USA.","DOI":"10.1109\/WSC.2017.8248028"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Beglerovic, H., Stolz, M., and Horn, M. (2017, January 16\u201320). Testing of autonomous vehicles using surrogate models and stochastic optimization. Proceedings of the IEEE Conference on Intelligent Transportation Systems, ITSC, Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317768"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"105664","DOI":"10.1016\/j.aap.2020.105664","article-title":"Safety assessment of highly automated driving systems in test tracks: A new framework","volume":"144","author":"Feng","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.1109\/TITS.2020.2988309","article-title":"Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies","volume":"22","author":"Feng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_78","unstructured":"Sthamer, H.-H. (1995). The Automatic Generation of Software Test Data Using Genetic Algorithms\u2014ProQuest, ProQuest Dissertations Publishing (University of South Wales)."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"B\u00fchler, O., and Wegener, J. (2004). Automatic Testing of an Autonomous Parking System Using Evolutionary Computation, Soceity of Automotive Engineers. SAE Technical Papers.","DOI":"10.4271\/2004-01-0459"},{"key":"ref_80","unstructured":"Buehler, O., and Wegener, J. (2005, January 22\u201326). Evolutionary Functional Testing of a Vehicle Brake Assistant System Introduction to Evolutionary Testing. Proceedings of the 6th Metaheuristics International Conference, Wien, Austria."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"O\u2019Kelly, M., Abbas, H., and Mangharam, R. (2017, January 18\u201322). Computer-aided design for safe autonomous vehicles. Proceedings of the 2017 Resilience Week, RWS 2017, Wilmington, DE, USA.","DOI":"10.1109\/RWEEK.2017.8088654"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.aap.2017.07.011","article-title":"Pre-crash scenarios at road junctions: A clustering method for car crash data","volume":"107","author":"Nitsche","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_83","unstructured":"Sandin, J. (2009). Analysis of intersection crash statistics to define pre-crash test scenarios for detection sensors. SAFER Report Project Scenario Based Testing of Pre-Crash Systems, SAFER-Vehicle and Traffic Safety Centre at Chalmers."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1177\/0361198119841285","article-title":"Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment","volume":"2673","author":"Liang","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"062034","DOI":"10.1088\/1757-899X\/263\/6\/062034","article-title":"Braking distance algorithm for autonomous cars using road surface recognition","volume":"263","author":"Kavitha","year":"2017","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_86","unstructured":"Greibe, P. (2008, January 13\u201317). Determination of Braking Distance and Driver Behaviour Based on Braking Trials. Proceedings of the 87th Transportation Research Board Annual Meeting, Washington DC, USA."},{"key":"ref_87","unstructured":"Kong, J., Pfeiffer, M., Schildbach, G., and Borrelli, F. (July, January 28). Kinematic and dynamic vehicle models for autonomous driving control design. Proceedings of the IEEE Intelligent Vehicles Symposium, Seoul, Korea."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","article-title":"A survey of motion planning and control techniques for self-driving urban vehicles","volume":"1","author":"Paden","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_89","unstructured":"(2021, August 09). UK GOV, Speed Limits\u2014GOV.UK, Available online: https:\/\/www.gov.uk\/speed-limits."},{"key":"ref_90","first-page":"1","article-title":"Analysis of Selection Schemes for Solving an Optimization Problem in Genetic Algorithm","volume":"93","author":"Sharma","year":"2014","journal-title":"Int. J. Comput. Appl."}],"container-title":["Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2674-113X\/1\/3\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:49:31Z","timestamp":1760140171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2674-113X\/1\/3\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,13]]},"references-count":90,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["software1030011"],"URL":"https:\/\/doi.org\/10.3390\/software1030011","relation":{},"ISSN":["2674-113X"],"issn-type":[{"type":"electronic","value":"2674-113X"}],"subject":[],"published":{"date-parts":[[2022,7,13]]}}}