Intelligent Perception Systems for Vehicles: A Review of Observation Technologies in Environmental Risk Mitigation and Resource Optimization

Authors

  • Min Li

    Guangxi Eco-Engineering Vocational & Technical College, Liuzhou City 545007, China
  • Qiaosheng Bo

    Guangxi Eco-Engineering Vocational & Technical College, Liuzhou City 545007, China
  • Junrong Huang

    Guangxi Eco-Engineering Vocational & Technical College, Liuzhou City 545007, China
  • Limiao Lu

    Guangxi Eco-Engineering Vocational & Technical College, Liuzhou City 545007, China

DOI:

https://doi.org/10.30564/jees.v8i7.13414
Received: 2 March 2026 | Revised: 23 April 2026 | Accepted: 27 April 2026 | Published Online: 8 July 2026

Abstract

Vehicles that have intelligent perception systems are the first in the new wave of innovation that is geared towards enhancing transportation safety, sustainability, and efficiency. Such systems are a collection of sensors, artificial intelligence (AI), machine learning (ML), and real-time data processing to allow vehicles to sense and understand their surroundings with a great level of accuracy. In this review, we discuss the use of intelligent perception technologies in reducing environmental hazards and leveraging resources to the fullest. It discusses important sensor technologies, such as cameras, Light Detection and Ranging (LiDAR), radar, and ultrasonic sensors, as well as AI-based decision-making systems that enable vehicles to respond to changing driving conditions. Intelligent perception systems can help reduce vehicle emissions and minimize the environmental effects of cars by optimizing driving behaviors and consuming less fuel. The significance of optimizing resources is also addressed in the review, with references to the applications of intelligent routing, eco-driving, and autonomous vehicle systems. Although these technologies are promising, there are challenges such as the integration of the technologies, issues related to sensor fusion, data privacy, and infrastructure upgrade that are required. Also, new technologies like 5G connectivity, edge computing, and sensor technology will develop further to add additional features to the intelligent perception systems. The article ends by highlighting that such technologies can be transformative in the pursuit of sustainable, efficient, and safe forms of transportation.

Keywords:

Intelligent Perception Systems; Environmental Risk Mitigation; Resource Optimization; Autonomous Vehicles; Sensor Fusion

References

[1] Pandharipande, A., Cheng, C.H., Dauwels, J., et al., 2023. Sensing and machine learning for automotive perception: A review. IEEE Sensors Journal. 23(11), 11097–11115.

[2] Olugbade, S., Ojo, S., Imoize, A.L., et al., 2022. A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications. 27(5), 77.

[3] Gangwani, D., Gangwani, P., 2021. Applications of Machine Learning and Artificial Intelligence in Intelligent Transportation System: A Review. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., et al. (Eds.). Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020. Springer: Singapore. pp. 203–216.

[4] Zhao, H., Li, X., Xu, C., et al., 2024. A survey of automatic driving environment perception. In Proceedings of the 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK, 1–5 July 2024; pp. 1038–1047.

[5] Kidd, D.G., Hagoski, B.K., Tucker, T.G., et al., 2015. The effectiveness of a rearview camera and parking sensor system alone and combined for preventing a collision with an unexpected stationary or moving object. Human Factors. 57(4), 689–700.

[6] Fayyad, J., Jaradat, M.A., Gruyer, D., et al., 2020. Deep learning sensor fusion for autonomous vehicle perception and localization: A review. Sensors. 20(15), 4220.

[7] Geronimo, D., López, A.M., Sappa, A.D., et al., 2009. Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(7), 1239–1258.

[8] Bayless, S.H., Guan, A., Son, P., et al., 2013. Connected Vehicle Insights: Trends in Roadway Domain Active Sensing: Developments in Radar, LIDAR and Other Sensing Technologies and Impact on Vehicle Crash Avoidance/Automation and Active Traffic Management. U.S. Department of Transportation (USDOT), Research and Innovative Technology Administration, ITS Joint Program Office: Washington, DC, USA.

[9] Tang, X., Gao, F., Xu, G., et al., 2014. Sensor systems for vehicle environment perception in a highway intelligent space system. Sensors. 14(5), 8513–8527.

[10] Grigorescu, S., Trasnea, B., Cocias, T., et al., 2020. A survey of deep learning techniques for autonomous driving. Journal of Field Robotics. 37(3), 362–386.

[11] Broggi, A., Zelinsky, A., Özgüner, Ü., et al., 2016. Intelligent vehicles. In: Siciliano, B., Khatib, O. (Eds.). Springer Handbook of Robotics. Springer: Cham, Switzerland. pp. 1627–1656.

[12] Rana, K., Khatri, N., 2024. Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review. Computers and Electrical Engineering. 117, 109237.

[13] Dey, K.C., Mishra, A., Chowdhury, M., 2014. Potential of intelligent transportation systems in mitigating adverse weather impacts on road mobility: A review. IEEE Transactions on Intelligent Transportation Systems. 16(3), 1107–1119.

[14] Vasebi, S., Hayeri, Y.M., Saghiri, A.M., 2023. A literature review of energy optimal adaptive cruise control algorithms. IEEE Access. 11, 13636–13646.

[15] Musa, A.A., Malami, S.I., Alanazi, F., et al., 2023. Sustainable traffic management for smart cities using internet-of-things-oriented intelligent transportation systems (ITS): Challenges and recommendations. Sustainability. 15(13), 9859.

[16] Golias, J., Yannis, G., Antoniou, C., 2002. Classification of driver-assistance systems according to their impact on road safety and traffic efficiency. Transport Reviews. 22(2), 179–196.

[17] Jiménez, F., Naranjo, J.E., Anaya, J.J., et al., 2016. Advanced driver assistance system for road environments to improve safety and efficiency. Transportation Research Procedia. 14, 2245–2254.

[18] Raffik, R., Maria, W.A., Subashini, B., et al., 2025. Artificial Intelligence-Based Predictive Maintenance Approaches for Vehicle Condition Monitoring and On-Board Diagnostic Systems to Enhance Automotive Industries. In: Mahalle, P.N., Shinde, G.R., Wasatkar, N.N., et al. (Eds.). Industry 5.0 for Society 5.0: Revolutionizing Smart Farming, Manufacturing, and Green Computing (Part 2). Bentham Science Publishers: Singapore. pp. 107–137.

[19] Reddy, K.B.N.K., Sravanthi, D.P.B., Reddy, E.J., et al., 2024. Recent AI applications in electrical vehicles for sustainability. International Journal of Mechanical Engineering. 11(3), 50–64.

[20] Nasir, M.K., Md Noor, R., Kalam, M.A., et al., 2014. Reduction of fuel consumption and exhaust pollutant using intelligent transport systems. The Scientific World Journal. 2014(1), 836375.

[21] Mancino, G., 2022. The Role of Autonomous Vehicles and AI in Smart Transportation Systems: Enhancing Traffic Management, Route Optimization, and Predictive Maintenance. DOI: https://doi.org/10.13140/RG.2.2.23868.04483

[22] Khalil, R.A., Safelnasr, Z., Yemane, N., et al., 2024. Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology. 5, 397–427.

[23] Kumar, P.G., Lekhana, P., Tejaswi, M., et al., 2021. Effects of vehicular emissions on the urban environment-a state of the art. Materials Today: Proceedings. 45(Part 7), 6314–6320.

[24] Azhar, U., Yaseen, S., Arif, M., et al., 2024. Emission of greenhouse gases from transportation. In: Rahimpour, M.R., Makarem, M.A., Meshksar, M. (Eds.). Advances and Technology Development in Greenhouse Gases: Emission, Capture and Conversion: Process Modelling and Simulation. Elsevier: Amsterdam, The Netherlands. pp. 147–163.

[25] Stanley, J.K., Hensher, D.A., Loader, C., 2011. Road transport and climate change: Stepping off the greenhouse gas. Transportation Research Part A: Policy and Practice. 45(10), 1020–1030.

[26] Zhang, Y., Tu, C., Gao, K., et al., 2024. Multisensor information fusion: Future of environmental perception in intelligent vehicles. Journal of Intelligent and Connected Vehicles. 7(3), 163–176.

[27] Marti, E., de Miguel, M.A., Garcia, F., et al., 2019. A review of sensor technologies for perception in automated driving. IEEE Intelligent Transportation Systems Magazine. 11(4), 94–108.

[28] Andronie, M., Lăzăroiu, G., Karabolevski, O.L.,et al., 2022. Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the internet of robotic things. Electronics. 12(1), 22.

[29] Mohammed, A.S., Amamou, A., Ayevide, F.K., et al., 2020. The perception system of intelligent ground vehicles in all weather conditions: A systematic literature review. Sensors. 20(22), 6532.

[30] Shahbazi, Z., Nowaczyk, S., 2023. Enhancing energy efficiency in connected vehicles for traffic flow optimization. Smart Cities. 6(5), 2574–2592.

[31] Hilmani, A., Maizate, A., Hassouni, L., 2020. Automated real-time intelligent traffic control system for smart cities using wireless sensor networks. Wireless Communications and Mobile Computing. 2020(1), 8841893.

[32] Hossain, M., Rahman, M., Ramasamy, D., 2024. Artificial intelligence-driven vehicle fault diagnosis to revolutionize automotive maintenance: A review. Computer Modeling in Engineering & Sciences. 141(2), 951–996.

[33] Zapata, C., Zemmouri, A., Bajit, A., Nieuwenhuis, P., 2010. Exploring innovation in the automotive industry: New technologies for cleaner cars. Journal of Cleaner Production. 18(1), 14–20.

[34] Barodi, A., Zemmouri, A., Bajit, A., et al., 2023. Intelligent transportation system based on smart soft-sensors to analyze road traffic and assist driver behavior applicable to smart cities. Microprocessors and Microsystems. 100, 104830.

[35] Todorovic, M., Simic, M., Kumar, A., 2017. Managing transition to electrical and autonomous vehicles. Procedia Computer Science. 112, 2335–2344.

[36] Ziegler, D., Abdelkafi, N., 2023. Exploring the automotive transition: A technological and business model perspective. Journal of Cleaner Production. 421, 138562.

[37] Guerrero-Ibáñez, J., Zeadally, S., Contreras-Castillo, J., 2018. Sensor technologies for intelligent transportation systems. Sensors. 18(4), 1212.

[38] Bente, T.F., Szeghalmy, S., Fazekas, A., 2018. Detection of lanes and traffic signs painted on road using on-board camera. In Proceedings of the 2018 IEEE International Conference on Future IoT Technologies (Future IoT), Eger, Hungary, 18–19 January 2018.

[39] Royo, S., Ballesta-Garcia, M., 2019. An overview of lidar imaging systems for autonomous vehicles. Applied Sciences. 9(19), 4093.

[40] Grimes, D.M., Jones, T.O., 2005. Automotive radar: A brief review. Proceedings of the IEEE. 62(6), 804–822.

[41] Sankareh, S., 2025. Smart Parking System Using Ultrasonic Sensors and ESP32-CAM [Bachelor's Thesis]. Savonia University of Applied Sciences: Kuopio, Finland.

[42] Ali, E.S., Hasan, M.K., Hassan, R., et al., 2021. Machine learning technologies for secure vehicular communication in internet of vehicles: Recent advances and applications. Security and Communication Networks. 2021(1), 8868355.

[43] Jebamikyous, H.-H., Kashef, R., 2022. Autonomous vehicles perception (AVP) using deep learning: Modeling, assessment, and challenges. IEEE Access. 10, 10523–10535.

[44] Vishnukumar, H.J., Butting, B., Müller, C., et al., 2017. Machine learning and deep neural network—Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation. In Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK, 7–8 September 2017.

[45] Rajeshkumar, G., Subramanian, K., Sadhasivam, N.R.V., et al., 2025. Autonomous Driving: Object Detection, Path Planning, and Decision Making using Deep Learning Model. In Proceedings of the 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 4–6 August 2025.

[46] Buch, N., Velastin, S.A., Orwell, J., 2011. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems. 12(3), 920–939.

[47] Dilek, E., Dener, M., 2023. Computer vision applications in intelligent transportation systems: A survey. Sensors. 23(6), 2938.

[48] Ahmed, S., Huda, M.N., Rajbhandari, S., et al., 2019. Pedestrian and cyclist detection and intent estimation for autonomous vehicles: A survey. Applied Sciences. 9(11), 2335.

[49] Butt, F.A., Chattha, J.N., Ahmad, J., et al., 2022. On the integration of enabling wireless technologies and sensor fusion for next-generation connected and autonomous vehicles. IEEE Access. 10, 14643–14668.

[50] Yeong, D.J., Velasco-Hernandez, G., Barry, J., et al., 2021. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors. 21(6), 2140.

[51] Yeong, D.J., Panduru, K., Walsh, J., 2025. Exploring the unseen: A survey of multi-sensor fusion and the role of explainable AI (XAI) in autonomous vehicles. Sensors. 25(3), 856.

[52] Song, H., Zhou, S., Chang, Z., et al., 2021. Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles. Assembly Automation. 41(3), 283–291.

[53] Xu, Y., Li, H., Liu, H., et al., 2017. Eco-driving for transit: An effective strategy to conserve fuel and emissions. Applied Energy. 194, 784–797.

[54] Dui, H., Zhang, S., Liu, M., et al., 2024. IoT-enabled real-time traffic monitoring and control management for intelligent transportation systems. IEEE Internet of Things Journal. 11(9), 15842–15854.

[55] Vaa, T., Penttinen, M., Spyropoulou, I., 2007. Intelligent transport systems and effects on road traffic accidents: State of the art. IET Intelligent Transport Systems. 1(2), 81–88.

[56] Abro, G.E.M., Zulkifli, S.A., Kumar, K., et al., 2023. Comprehensive review of recent advancements in battery technology, propulsion, power interfaces, and vehicle network systems for intelligent autonomous and connected electric vehicles. Energies. 16(6), 2925.

[57] Viswanathan, B., Shanmugam, 2026. AI for Vehicle Route Optimization: Developing and Implementing Machine Learning Algorithms to Minimize Travel Time, Fuel Consumption, and Vehicle Emissions for Waste Collection Fleets. Vaagai International Publishing House: Coimbatore, India.

[58] Singh, S., Singh, J., Goyal, S.B., et al., 2023. A novel framework to avoid traffic congestion and air pollution for sustainable development of smart cities. Sustainable Energy Technologies and Assessments. 56, 103125.

[59] Jagatheesaperumal, S.K., Bibri, S.E., Huang, J., et al., 2024. Artificial intelligence of things for smart cities: Advanced solutions for enhancing transportation safety. Computational Urban Science. 4(1), 10.

[60] Gowri, B.S., Priya, G.V., Anjana, S., et al., 2024. A Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep Learning Techniques. In Proceedings of the International Conference on Intelligent Systems and Sustainable Computing, Canberra, Australia, 9–10 September 2024.

[61] Ul Haq, I., Ali, S., Shahani, S.A., et al., 2026. Artificial Intelligence and Machine Learning in Smart Transportation Systems: Improving Road Safety, Traffic Flow, and Environmental Sustainability. Global Research Journal of Natural Science and Technology. 3(4). DOI: https://doi.org/10.53762/grjnst.03.04.01

[62] Finogeev, A., Finogeev, А., Fionova, L., et al., 2019. Intelligent monitoring system for smart road environment. Journal of Industrial Information Integration. 15, 15–20.

[63] Birrell, S.A., Fowkes, M., Jennings, P.A., 2014. Effect of using an in-vehicle smart driving aid on real-world driver performance. IEEE Transactions on Intelligent Transportation Systems. 15(4), 1801–1810.

[64] Fafoutellis, P., Mantouka, E.G., Vlahogianni, E.I., 2020. Eco-driving and its impacts on fuel efficiency: An overview of technologies and data-driven methods. Sustainability. 13(1), 226.

[65] Gonder, J., Earleywine, M., Sparks, W., 2012. Analyzing vehicle fuel saving opportunities through intelligent driver feedback. SAE International Journal of Passenger Cars-Electronic and Electrical Systems. 5(2), 450–461.

[66] Ayyildiz, K., Cavallaro, F., Nocera, S., et al., 2017. Reducing fuel consumption and carbon emissions through eco-drive training. Transportation Research Part F: Traffic Psychology and Behaviour. 46, 96–110.

[67] Rinchi, O., Alsharoa, A., Shatnawi, I., et al., 2024. The role of intelligent transportation systems and artificial intelligence in energy efficiency and emission reduction. arXiv Preprint. arXiv:2401.14560. DOI: https://doi.org/10.48550/arXiv.2401.14560

[68] Li, D., Deng, L., Cai, Z., 2020. Intelligent vehicle network system and smart city management based on genetic algorithms and image perception. Mechanical Systems and Signal Processing. 141, 106623.

[69] Yu, L., Wang, R., 2022. Researches on Adaptive Cruise Control system: A state of the art review. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 236(2–3), 211–240.

[70] Haque, T.S., Rahman, M.H., Islam, M.R., et al., 2021. A review on driving control issues for smart electric vehicles. IEEE Access. 9, 135440–135472.

[71] Djahel, S., Doolan, R., Muntean, G.-M., et al., 2014. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials. 17(1), 125–151.

[72] Lu, K., Han, B., Zhou, X., 2018. Smart urban transit systems: From integrated framework to interdisciplinary perspective. Urban Rail Transit. 4(2), 49–67.

[73] Xu, M., Liu, T., Zhong, S., et al., 2022. Urban smart public transport studies: A review and prospect. Journal of Transportation Systems Engineering and Information Technology. 22(2), 91–108. (in Chinese)

[74] Taiebat, M., Brown, A.L., Stafford, H.R., et al., 2018. A review on energy, environmental, and sustainability implications of connected and automated vehicles. Environmental Science & Technology. 52(20), 11449–11465.

[75] Gružauskas, V., Baskutis, S., Navickas, V., 2018. Minimizing the trade-off between sustainability and cost effective performance by using autonomous vehicles. Journal of Cleaner Production. 184, 709–717.

[76] Bathla, G., Bhadane, K., Singh, R.K., et al., 2022. Autonomous vehicles and intelligent automation: Applications, challenges, and opportunities. Mobile Information Systems. 2022(1), 7632892.

[77] Jamil, H., Naqvi, S.S.A., Iqbal, N., et al., 2024. Analysis on the driving and braking control logic algorithm for mobility energy efficiency in electric vehicle. Smart Grids and Sustainable Energy. 9(1), 12.

[78] Miles, J., Walker, A.J., 2006. The potential application of artificial intelligence in transport. IEE Proceedings-Intelligent Transport Systems. 153(3), 183–198.

[79] Meiring, G.A.M., Myburgh, H.C., 2015. A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors. 15(12), 30653–30682.

[80] Bouchelaghem, S., Omar, M., 2018. Reliable and secure distributed smart road pricing system for smart cities. IEEE Transactions on Intelligent Transportation Systems. 20(5), 1592–1603.

[81] Jamshed, M.A., Ali, K., Abbasi, Q.H., et al., 2022. Challenges, applications, and future of wireless sensors in Internet of Things: A review. IEEE Sensors Journal. 22(6), 5482–5494.

[82] Jaradat, M., Jarrah, M., Bousselham, A., et al., 2015. The internet of energy: Smart sensor networks and big data management for smart grid. Procedia Computer Science. 56, 592–597.

[83] Wang, Y., Chung, S.-H., 2022. Artificial intelligence in safety-critical systems: A systematic review. Industrial Management & Data Systems. 122(2), 442–470.

[84] Lim, H.S.M., Taeihagh, A., 2018. Autonomous vehicles for smart and sustainable cities: An in-depth exploration of privacy and cybersecurity implications. Energies. 11(5), 1062.

[85] Pavithra, R., Kaliappan, V.K., Rajendar, S., 2023. Security algorithm for intelligent transport system in cyber-physical systems perceptive: Attacks, vulnerabilities, and countermeasures. SN Computer Science. 4(5), 544.

[86] Danezis, G., Domingo-Ferrer, J., Hansen, M., et al., 2015. Privacy and data protection by design-from policy to engineering. arXiv Preprint. arXiv:1501.03726. DOI: https://doi.org/10.48550/arXiv.1501.03726

[87] Garikapati, D., Poovalingam, S., Hau, W., et al., 2024. A comprehensive review of parallel autonomy systems within vehicles: Applications, architectures, safety considerations, and standards. IEEE Access. 12, 150395–150418.

[88] Liyanage, M., Thakur, V., 2026. 5g-Enabled Mobile Applications: Revolutionizing Mobile Technology through High-Speed Connectivity, Low Latency, and Enhanced User Experiences. Journal of Android, IOS Development and Testing. 10(3), 123–132.

[89] Ford, R., Zhang, M., Mezzavilla, M., et al., 2017. Achieving ultra-low latency in 5G millimeter wave cellular networks. IEEE Communications Magazine. 55(3), 196–203.

[90] Ma, Y., Wang, Z., Yang, H., et al., 2020. Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA Journal of Automatica Sinica. 7(2), 315–329.

Downloads

How to Cite

Li, M., Bo, Q., Huang, J., & Lu, L. (2026). Intelligent Perception Systems for Vehicles: A Review of Observation Technologies in Environmental Risk Mitigation and Resource Optimization. Journal of Environmental & Earth Sciences, 8(7), 48–67. https://doi.org/10.30564/jees.v8i7.13414

Issue

Vol. 8 , Iss. 7 (July 2026): In Progress

Article Type

Review

License

Copyright © 2026 Min Li, Qiaosheng Bo, Junrong Huang, Limiao Lu

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC