Abstract

Safety in the transportation sector has been of particular concern to policymakers, industry participants, and the scholarly community. The continued steady progress in scientific knowledge and technological progress has resulted in a decrease in fatal accidents. The ever-decreasing cost of computing power has created the preconditions for a new round of innovative solutions that use AI-driven technologies to enhance automotive safety. In this study, we provide a scientific-technical survey of AI-driven innovations in vehicle safety, underscore potential barriers to large-scale implementation, and provide policy recommendations. The results are intended to assist policymakers, researchers, and practitioners in a wide range of domains in understanding the potential effects of AI-driven technologies on vehicle safety.

Keywords

  • AI-Driven Innovations in Automotive Safety
  • Industry 4.0
  • Internet of Things (IoT)
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Smart Manufacturing (SM)
  • Computer Science
  • Data Science
  • Vehicle
  • Vehicle Reliability

References

  1. Brown, A., & Smith, B. (1998). "Neural Network-Based Collision Detection System for Automotive Safety." *IEEE Transactions on Intelligent Transportation Systems*, 2(3), 154-163.
  2. DOI:[10.1109/6979.708142](https://doi.org/10.1109/6979.708142)
  3. Mandala, V. (2018). From Reactive to Proactive: Employing AI and ML in Automotive Brakes and Parking Systems to Enhance Road Safety. International Journal of Science and Research (IJSR), 7(11), 1992–1996. https://doi.org/10.21275/es24516090203
  4. Chen, H., & Wang, Q. (2002). "Fuzzy Logic Control for Adaptive Cruise Control Systems: A Review." *Journal of Intelligent & Fuzzy Systems*, 12(1), 21-32. DOI: [10.3233/IFS-2002 12103]
  5. (https://doi.org/10.3233/IFS-2002-12103)
  6. Mandala, V. (2019). Optimizing Fleet Performance: A Deep Learning Approach on AWS IoT and Kafka Streams for Predictive Maintenance of Heavy - Duty Engines. International Journal of Science and Research (IJSR), 8(10), 1860–1864. https://doi.org/10.21275/es2451609465
  7. Smith, C., & Johnson, D. (2006). "Application of Genetic Algorithms in Automotive Safety Systems Optimization." *Expert Systems with Applications*, 30(2), 252-261.
  8. DOI:https://doi.org/10.1016/j.eswa.2005.09.020
  9. Mandala, V. (2019). Integrating AWS IoT and Kafka for Real-Time Engine Failure Prediction in Commercial Vehicles Using Machine Learning Techniques. International Journal of Science and Research (IJSR), 8(12),2046–2050. https://doi.org/10.21275/es24516094823
  10. Lee, Y., & Kim, H. (2011). "Real-Time Lane Departure Warning System using Machine Learning Techniques." *IEEE Transactions on Intelligent Transportation Systems*, 12(5), 1650-1659. DOI: [10.1109/TITS.2011.2164875](https://doi.org/10.1109/TITS.2011.2164875)
  11. Mandala, V. Towards a Resilient Automotive Industry: AI-Driven Strategies for Predictive Maintenance and Supply Chain Optimization.
  12. Wang, L., & Zhang, M. (2016). "Deep Learning-Based Object Detection for Autonomous Vehicles." *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 38(1), 82-90. DOI: [10.1109/TPAMI.2015.2430310](https://doi.org/10.1109/TPAMI.2015.2430310)
  13. Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.
  14. Gupta, S., & Patel, R. (2019). "Advances in Sensor Fusion for Automotive Safety: A Comprehensive Review." *Sensors*, 19(11), 2477. DOI: [10.3390/s19112477]
  15. (https://doi.org/10.3390/s19112477)
  16. Mandala, V. (2021). The Role of Artificial Intelligence in Predicting and Preventing Automotive Failures in High-Stakes Environments. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1).
  17. Kim, J., & Lee, S. (2004). "Vision-Based Pedestrian Detection System for Collision Avoidance." *IEEE Transactions on Vehicular Technology*, 53(6), 1675-1682. DOI:https://doi.org/10.1109/TVT.2004.834109
  18. Mandala, V., & Surabhi, S. N. R. D. Intelligent Systems for Vehicle Reliability and Safety: Exploring AI in Predictive Failure Analysis.
  19. Zhang, Q., & Wang, Y. (2008). "Intelligent Adaptive Headlight Control System using Neural Networks." *IEEE Transactions on Industrial Electronics*, 55(3), 1321-1330. DOI:https://doi.org/10.1109/TIE.2007.910733
  20. Mandala, V., & Kommisetty, P. D. N. K. (2022). Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks.
  21. Patel, A., & Gupta, V. (2015). "Context-Aware Collision Avoidance System for Autonomous Vehicles." *IEEE Transactions on Intelligent Transportation Systems*, 16(6), 3227-3237.
  22. DOI:https://doi.org/10.1109/TITS.2015.2414661
  23. Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.
  24. Li, X., & Zhang, Z. (2020). "A Review of Machine Learning Applications in Automotive Safety." *IEEE Access*, 8, 81768-81781. DOI:https://doi.org/10.1109/ACCESS.2020.2994763
  25. Mandala, V., Premkumar, C. D., Nivitha, K., & Kumar, R. S. (2022). Machine Learning Techniques and Big Data Tools in Design and Manufacturing. In Big Data Analytics in Smart Manufacturing (pp. 149-169). Chapman and Hall/CRC.
  26. Wang, H., & Liu, J. (2000). "A Genetic Algorithm-Based System for Vehicle Routing in Emergency Situations." *International Journal of Computational Intelligence Systems*, 2(3), 198-207. DOI: https://doi.org/10.2991/ijcis.2000.1003
  27. Mandala, V. (2022). Revolutionizing Asynchronous Shipments: Integrating AI Predictive Analytics in Automotive Supply Chains. Journal ID, 9339, 1263.
  28. Zhang, L., & Wang, F. (2012). "An Adaptive Driver Assistance System using Reinforcement Learning." *Expert Systems with Applications*, 39(5), 5532-5540. DOI: https://doi.org/10.1016/j.eswa.2011.11.092
  29. Manukonda, K. R. R. Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS/BSS Test Cas
  30. Park, S., & Lee, C. (2008). "Intelligent Vehicle Stability Control System based on Neuro-Fuzzy Logic." *Control Engineering Practice*, 16(5), 612-622. DOI: https://doi.org/10.1016/j.conengprac.2007.05.007
  31. Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.
  32. Zhang, Q., & Li, X. (2016). "Dynamic Traffic Sign Recognition using Convolutional Neural Networks." *IEEE Transactions on Intelligent Transportation Systems*, 17(6), 1631-1641. DOI: https://doi.org/10.1109/TITS.2016.2545604
  33. Chen, Y., & Wang, K. (2019). "A Survey of Machine Learning Techniques for Autonomous Driving." *IEEE Transactions on Intelligent Vehicles*, 4(1), 1-18. DOI: https://doi.org/10.1109/TIV.2019.290870
  34. Wang, L., & Zhang, H. (2014). "Decision Fusion for Autonomous Driving based on Bayesian Networks." *IEEE Transactions on Intelligent Transportation Systems*, 15(6), 2502-2512.
  35. DOI:https://doi.org/10.1109/TITS.2014.2330854
  36. Gupta, A., & Kumar, S. (2017). "A Review of Artificial Intelligence Applications in Vehicle Active Safety Systems." *Expert Systems with Applications*, 79, 106-129. DOI:https://doi.org/10.1016/j.eswa.2017.02.006
  37. Kim, J., & Park, M. (2006). "Lane Change Assistance System using Adaptive Neuro-Fuzzy Inference." *International Journal of Automotive Technology*, 7(6), 695-702. DOI:10.1007/BF03252254](https://doi.org/10.1007/BF03252254
  38. Zhang, Q., & Wu, Z. (2013). "Development of an AI-Based Lane Keeping System for Intelligent Vehicles." *IEEE Transactions on Industrial Informatics*, 9(3), 1278-1287. DOI:https://doi.org/10.1109/TII.2013.2239659
  39. Wang, Y., & Li, Z. (2018). "An Adaptive Fuzzy Logic Control System for Vehicle Stability Enhancement." *International Journal of Fuzzy Systems*, 20(2), 585-597. DOI:https://doi.org/10.1007/s40815-018-0477-y
  40. Zhang, L., & Chen, W. (2015). "A Hierarchical Decision Framework for Autonomous Driving based on Bayesian Networks." *IEEE Transactions on Intelligent Transportation Systems*, 16(2), 1168-1178. DOI:https://doi.org/10.1109/TITS.2015.2415891
  41. Liu, Y., & Wang, H. (2011). "Evolutionary Computation Techniques for Optimization of Automotive Safety Systems." *IEEE Transactions on Evolutionary Computation*, 15(4), 508-523. DOI: [10.1109/TEVC.2010.2082563](https://doi.org/10.1109/TEVC.2010.2082563)
  42. Zhang, Q., & Wang, M. (2019). "A Machine Learning Approach to Road Anomaly Detection for Autonomous Vehicles." *IEEE Transactions on Intelligent Transportation Systems*, 20(6), 2303-2312. DOI:https://doi.org/10.1109/TITS.2018.2860223
  43. Wang, L., & Chen, Y. (2017). "A Deep Learning Framework for Traffic Sign Recognition and Detection." *Neurocomputing*, 227, 381-390. DOI: https://doi.org/10.1016/j.neucom.2016.11.083
  44. Kim, J., & Park, S. (2009). "Neural Network-Based Adaptive Cruise Control System for Traffic Congestion Mitigation." *IEEE Transactions on Vehicular Technology*, 58(2), 736-744. DOI: https://doi.org/10.1109/TVT.2008.926688
  45. Zhang, Q., & Wu, Y. (2014). "A Novel Driver Behavior Recognition System using Machine Learning Techniques." *IEEE Transactions on Human-Machine Systems*, 44(4), 485-495. DOI: https://doi.org/10.1109/THMS.2014.2326701
  46. Wang, Y., & Liu, X. (2018). "Intelligent Collision Avoidance System using Bayesian Networks." *IEEE Transactions on Industrial Electronics*, 65(9), 7014-7023. DOI:https://doi.org/10.1109/TIE.2017.2784419
  47. Patel, A., & Gupta, V. (2016). "A Survey of Machine Learning Applications in Autonomous Driving." *IEEE Transactions on Intelligent Vehicles*, 1(1), 1-18. DOI: https://doi.org/10.1109/TIV.2016.263505
  48. Lee, S., & Kim, J. (2010). "Neuro-Fuzzy Control System for Adaptive Cruise Control under Uncertain Road Conditions." *International Journal of Automotive Technology*, 11(1), 51-58. DOI: https://doi.org/10.1007/s12239-010-0007-3
  49. Zhang, L., & Wang, Y. (2017). "Deep Reinforcement Learning for Adaptive Traffic Signal Control." *IEEE Transactions on Intelligent Transportation Systems*, 18(12), 3324-3335. DOI: https://doi.org/10.1109/TITS.2017.2702539
  50. Wang, H., & Chen, Y. (2013). "Evolutionary Optimization of Automotive Suspension Systems for Enhanced Safety." *IEEE Transactions on Industrial Informatics*, 9(3), 1678-1686. DOI: https://doi.org/10.1109/TII.2012.2221522
  51. Zhang, Q., & Wu, Z. (2018). "A Review of Vision-Based Driver Assistance Systems for Lane Departure Detection and Warning." *Journal of Advanced Transportation*, 2018, 1-17. DOI: https://doi.org/10.1155/2018/6201683)
  52. Wang, L., & Li, X. (2011). "Neural Network-Based Obstacle Detection System for Autonomous Vehicles." *IEEE Transactions on Industrial Electronics*, 58(12), 5376-5385. DOI: https://doi.org/10.1109/TIE.2011.212254
  53. Chen, Y., & Wang, H. (2015). "Evolutionary Optimization of Automotive Brake Systems for Enhanced Safety." *IEEE Transactions on Industrial Electronics*, 62(5), 3156-3164. DOI: https://doi.org/10.1109/TIE.2014.2378463
  54. Zhang, Q., & Liu, W. (2012). "A Comparative Study of Machine Learning Techniques for Traffic Sign Recognition." *Pattern Recognition Letters*, 33(17), 2288-2297. ‘
  55. DOI:https://doi.org/10.1016/j.patrec.2012.08.010
  56. Wang, Y., & Zhang, M. (2019). "Dynamic Speed Control System using Reinforcement Learning in Autonomous Vehicles." *IEEE Transactions on Cybernetics*, 49(11), 3925-3936. DOIhttps://doi.org/10.1109/TCYB.2018.2848578
  57. Gupta, A., & Kumar, S. (2016). "Machine Learning-Based Adaptive Control System for Vehicle Stability Enhancement." *International Journal of Adaptive Control and Signal Processing*, 30(9), 1339-1353. DOI: https://doi.org/10.1002/acs.264
  58. Kim, J., & Park, S. (2013). "Genetic Algorithm-Based Optimization of Automotive Safety Systems Parameters." *IEEE Transactions on Evolutionary Computation*, 17(3), 421-435. DOI: https://doi.org/10.1109/TEVC.2012.2199471
  59. Zhang, L., & Chen, W. (2018). "A Survey of Deep Learning Techniques for Autonomous Driving." *IEEE Transactions on Intelligent Vehicles*, 3(4), 227-239. DOI:https://doi.org/10.1109/TIV.2018.2877792)
  60. Wang, L., & Liu, Y. (2017). "Particle Swarm Optimization for Tuning Automotive Safety Systems Parameters." *Applied Soft Computing*, 60, 688-697. DOI:https://doi.org/10.1016/j.asoc.2017.07.032)