Abstract
To bring autonomous vehicles on the road requires modern technology which promises precise sensing of the different parameters and accurately using the collected set of information for the course of action. Sensing of the surrounding parameters includes system understanding signal and lighting systems, identifying hazardous situations, distinguishing different obstacles, and according to activating different applications like blind-spot detection, antilock braking, airbags, tire pressure monitoring, battery level monitoring for electric vehicles, downhill control, cruise controlling, emergency braking and many other applications. To implement the titled architecture a case study of the automatic braking system is implemented using a machine learning approach. Specific identification of the car is done using the Haar-Cascade Algorithm. The module is trained by giving numerous positive and negative images. The large set of the data is stored in the Hierarchical Data Format 5 version of the HDF5 file format. The XML file and HDF5 files are then imported and a new video stream for identification of the car and brake light is fed to the module. The prediction of the module is done in four different classes such as brake applied, brake not applied, parking light, Left or right indicator, and light off state. The proposed module identifies the brake light of the car with 99% accuracy.
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References
Mohamed, N.: Elghitany, Farid Tolba, Adham Mohamed Abdelkader”, Low ehicle speeds regenerative anti-lock braking system”. Ain Shams Eng. J. (2021). https://doi.org/10.1016/j.asej.2021.08.013
Weis, T., Mundt, M., Harding, P., Ramesh, V.: Anomaly detection for automotive visual signal transition estimation. IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8 (2017). https://doi.org/10.1109/ITSC.2017.8317605
Svärd, M., Markkula, G., Bärgman, J., Victor, T.: Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes. Accid. Anal. Prev. 163, 106433 (2021). https://doi.org/10.1016/j.aap.2021.106433
Davoodi, S.R., Hamid, H.: Motorcyclist braking performance in stopping distance situations. J. Transp. Eng. 139(7), 660–666 (2013). https://doi.org/10.1061/(ASCE)TE.1943-5436.0000552
Kim, D., Eo, J.S., Kim, K.-K.K.: Service-oriented real-time energy-optimal regenerative braking strategy for connected and autonomous electrified vehicles. IEEE Trans. Intell. Transp. Syst. 23, 11098–11115 (2021). https://doi.org/10.1109/TITS.2021.3099812
Ciberlin, J., Grbic, R., Teslić, N., Pilipović, M.: Object detection and object tracking in front of the vehicle using front view camera. Zooming Innovation in Consumer Technologies Conference (ZINC) pp. 27–32 (2019). https://doi.org/10.1109/ZINC.2019.8769367
Savino, G., Pierini, M., Thompson, J., Fitzharris, M., Lenné, M.G.: Exploratory Field Trial of Motorcycle Autonomous Emergency Braking (MAEB): Considerations on The Acceptability of Unexpected Automatic Decelerations. Traffic Inj. Prev. 17(8), 855–862 (2016). https://doi.org/10.1080/15389588.2016.1155210
Thammakaroon, P, Tangamchit, P.: Predictive brake warning at night using taillight characteristic. IEEE Int. Symp. Ind. Electron. (2009). https://doi.org/10.1109/ISIE.2009.5218254
Kim, S.Y., et al.: Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion. IEEE/RSJ Int. Conf. Intell. Robots Syst. (2005). https://doi.org/10.1109/IROS.2005.1545321
Savino, G., Giovannini, F., Baldanzini, N., Pierini, M., Rizzi, M.: Assessing the potential benefits of the motorcycle autonomous emergency braking using detailed crash reconstructions. Traffic Inj. Prev. S40–S49 (2013). https://doi.org/10.1080/15389588.2013.803280
Siddiqi, K., Raza, A.D., Muhammad, S.S.: Visible light communication for V2V intelligent transport system. 2016 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom) pp. 1–4 (2016). https://doi.org/10.1109/COBCOM.2016.7593510
Siebert, F.W., Ringhand, M., Englert, F., Hoffknecht, M., Edwards, T., Rotting, M.: Braking bad – Ergonomic design and implications for the safe use of shared E-scooters. Saf. Sci. 140, 105294 (2021). https://doi.org/10.1016/j.ssci.2021.105294
Shimazaki, K., Ito, T., Fujii, Ai., Ishida, T.: The public’s understanding of the functionality and limitations of automatic braking in Japan. Int. Assoc. Traffic Saf. Sci. 42(4), 221–229 (2018). https://doi.org/10.1016/j.iatssr.2017.11.002
Satzoda, R.K., Trivedi, M.M.: Looking at Vehicles in the Night: Detection and Dynamics of Rear Lights. IEEE Trans. Intell. Transp. Syst. 20(12), 4297–4307 (2019). https://doi.org/10.1109/TITS.2016.2614545
Tiwari, A., Karthikeyan, B., Suresh, S.: Testing and implementation of smart brake pedal system with signal diagnostic and failure detection. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking. pp. 1–3 (2019). https://doi.org/10.1109/ViTECoN.2019.8899361
Chen, D.-Y., Chen, C.-H.: Salient video cube guided nighttime vehicle braking event detection. J. Vis. Commun. Image Represent. 23, 586–597 (2012). https://doi.org/10.1016/j.jvcir.2012.01.013
Wang, J., et al.: Appearance-based Brake-Lights recognition using deep learning and vehicle detection. IEEE Intelligent Vehicles Symposium (IV), pp. 815–820 (2016). https://doi.org/10.1109/IVS.2016.7535481
Rapson, C.J., Seet, B., Naeem, M.A., Lee, J.E., Klette, R.: A performance comparison of deep learning methods for real-time localisation of vehicle lights in video frames. IEEE Intelligent Transportation Systems Conference (ITSC), pp. 567–572 (2019). https://doi.org/10.1109/ITSC.2019.8917087
Chen, D., Peng, Y.: Frequency-tuned taillight-based nighttime vehicle braking warning system. IEEE Sens. J. 12(11), 3285–3292 (2012). https://doi.org/10.1109/JSEN.2012.2212971
Edwards, M., Nathanson, A., Wisch, M.: Estimate of potential benefit for Europe of fitting Autonomous Emergency Braking (AEB) systems for pedestrian protection to passenger cars. Traffic Inj. Prev. 173–182 (2014). https://doi.org/10.1080/15389588.2014.931579
Fowler, G. F., Ray, R. M., Huang, S., Zhao, K., and Frank, T. A.: An examination of motorcycle antilock brake systems in reducing crash risk. ASME. ASME J. Risk Uncertain. Part B. 2(2), 021006 (2016). https://doi.org/10.1115/1.4031522
Lucci, C., Marra, M., Huertas-Leyva, P., Baldanzini, N., Savino, G.: Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking. MethodsX 8, 101225 (2021). https://doi.org/10.1016/j.mex.2021.101225
Srikanth, S., Dhivya, S., Anisha, R., Hariharan, S.: An IOT approach to vehicle accident detection using cloud computing. 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 1009–1011 (2019). https://doi.org/10.1109/ICACCS.2019.8728457
Wang, J, Zhou, L, Song, Z., Yuan, M.: Real-time vehicle signal lights recognition with HDR camera. IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications and Cyber, Physical and Social Computing and Smart Data. pp. 355–358 (2016). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.84.
Savino, G.: Pierini, Marco, Rizzi, Matteo & Frampton, Richard. Evaluation of an Autonomous Braking System in Real-World PTW Crashes. Traffic Inj. Prev. 14, 532–543 (2013). https://doi.org/10.1080/15389588.2012.725878
Wang, X., Tang, J., Niu, J., Zhao, X.: Vision-based two-step brake detection method for vehicle collision avoidance. Neurocomputing (2015). https://doi.org/10.1016/j.neucom.2015.04.117
Xiang, W., Yan, X., Weng, J., Li, X.: Effect of auditory in-vehicle warning information on drivers’ brake response time to red-light running vehicles during collision avoidance. Transport. Res. F: Traffic Psychol. Behav. 40, 56–67 (2016). https://doi.org/10.1016/j.trf.2015.12.002
Chen, J., et al.: Mining urban sustainable performance: GPS data-based spatio-temporal analysis on on-road braking emission”. Journal of Cleaner Production 270, 122489 (2020)
Jian, G., Liu, A., Yu, C., Zhou, P.: A multiagent based warning system for pedestrian safety. International Conference on Advanced Robotics and Intelligent Systems (ARIS), pp. 41–45 (2014). https://doi.org/10.1109/ARIS.2014.6871524
Savino, G., Giovannini, F., Baldanzini, N., Pierini, M., Rizzi, M.: Assessing the potential benefits of the motorcycle autonomous emergency braking using detailed crash reconstructions. Traffic Inj. Prev. 14, S40–S49 (2013). https://doi.org/10.1080/15389588.2013.803280
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The authors would like to express sincere thanks to my guide Dr. Sudhir Kanade
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Jagtap, S.D., Kanade, S.S. Improving Run Time Efficiency of Semantic Video Event Classification. Int. J. ITS Res. 21, 12–25 (2023). https://doi.org/10.1007/s13177-022-00333-1
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DOI: https://doi.org/10.1007/s13177-022-00333-1