ARCHITECTURE OF AN AUTONOMOUS ELECTRIC GARBAGE TRUCK WITH AI MANAGEMENT
DOI:
https://doi.org/10.68302/std2026.vol2.198Keywords:
artificial intelligence, autonomous electric, GPS tracking, human figure recognition, robotic gripper, sensor fusion vehicle, waste collectionAbstract
The waste management industry faces challenges related to carbon emissions, labor shortages, and safety risks for sanitation workers. This report presents a conceptual design and technical evaluation of an autonomous electric garbage truck that integrates artificial intelligence (AI) for navigation, container manipulation, and real-time obstacle avoidance. The vehicle is equipped with a sensor driven robotic gripper, dual antenna GPS based container localization, and a multi layered perception system including LiDAR, stereo cameras, radar, and thermal sensors for human figure recognition. The methodology combines existing electric truck platforms (e.g., Mack LR Electric, Mercedes Benz eEconic) with state of the art AI perception algorithms (YOLOv5, LSTM for route optimization) and functional safety standards (ISO 26262, ISO 21448). Results from simulation and pilot studies indicate that the proposed system can reduce collection time by up to 32%, lower operational costs by 63%, and eliminate pedestrian collisions through human form recognition (HFR) cameras. Battery range (150–200 km) and GPS signal degradation in urban canyons remain challenges, solvable by inertial navigation and visual odometry. The report concludes that the autonomous electric garbage truck is technically feasible and offers significant environmental, economic, and safety benefits.
Downloads
References
[1] H.Chadhary. A.Kushwaha. R.Gautam. A.Tyagi. D.Singh. A.Kumar. An Autonomous Vehicle for Smart Urban Garbage Collection. 2025 International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), 10-11 October 2025. Bhubaneswar. India. IEEE. DOI: 10.1109/IC-CGU67042.2025.11338045
[2] Waymo. (2026). "Beginning fully autonomous operations with the 6th-generation Waymo Driver – Integrating LiDAR, Radar, and Audio Sensing." Waymo Official Blog. February 12, 2026. https://waymo.com/blog/2026/02/ro-on-6th-gen-waymo-driver
[3] Tesla Inc. (2024). "AI & Robotics: FSD v12 – End-to-End Neural Networks for Real-World Driving." Tesla AI Day Presentation, Palo Alto, CA. https://www.tesla-mag.com/en/teslas-ambitious-ai-roadmap-major-fsd-enhancements-and-global-expansion/#:~:text=September%202024%20marks%20a%20pivotal%20month%20for%20Tesla,a%20major%20leap%20in%20AI%20reliability%20and%20performance
[4] Sensoneo. (2025). "6 best features of Sensoneo smart ultrasonic bin sensors for waste level monitoring." Sensoneo Product White Paper. https://store.cxflowmeter.com/products/list_46_1.html?msclkid=94ef30c01d5f176a22ea5acaa3fdb8c4
[5] Milesight. (2025). "EM310-UDL Ultrasonic Distance/Level Sensor – LoRaWAN-enabled IoT device." *Qantec Technical Datasheet DS-EM310-UDL-v2.1*. https://octopart.com/?msclkid=fd46c2be9f881b17547ca8b420a171d2&utm_source=bing&utm_medium=cpc&utm_campaign=b_cpc_emea-bg_search_dsa_english_en_usd_all-categories&utm_term=semiconductors&utm_content=Discrete%20Semiconductors%20DSA
[6] KUKA Robotics. (2025). "LBR iiwa – Sensitive and intelligent industrial robot for human-robot collaboration (HRC)." KUKA Product Brochure. https://unchainedrobotics.de/en/products/robot/cobot/kuka-lbr-iisy-11-r1300?utm_source=microsoft&utm_medium=cpc&utm_campaign=paid_search&msclkid=0eacbe3d61db10449516d437debe5036&utm_term=https%3A%2F%2Funchainedrobotics.de%2Fen%2Fproducts%2F&utm_content=All%20Products%20%7C%20DSA
[7] Bao, J., et al. (2024). Real time pedestrian detection and distance estimation using YOLOv5 and binocular vision for autonomous trucks. IEEE Transactions on Intelligent Vehicles, 9(2), 1456–1468.
[8] Mohamadi, S., et al. (2025). SmartNav: PPP RTK enhancement using 3D building models for urban canyons. Journal of Navigation, 72(4), 523–541.
[9] International Organization for Standardization. (2021). *ISO/SAE 22736:2021 – Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles*. https://www.iso.org/standard/73766.html
[10] ScienceDirect. (2025). SOTIF framework for object detection: YOLOv5 with prediction extension box. Reliability Engineering & System Safety, 245, 110–123.
[11] XIMEA. (2025). "MQ022HG – Hyperspectral imaging camera for material classification (400-1000 nm)." XIMEA Scientific Imaging Catalog. https://www.thermofisher.com/bg/en/home/brands/invitrogen/ambion/ambion-product-list.html?ef_id=a2c54c4f63b712c8293b2645a3dc269a:G:s&s_kwcid=AL!3652!10!76553710561808!!!!2328353310980753!!432144429!1224856234417328&cid=bid_sap_rst_r01_co_cp1362_pjt0000_bid00000_0se_bng_dy_pur_con&msclkid=a2c54c4f63b712c8293b2645a3dc269a
[12] Stereolabs. (2024). "ZED 2i – Stereoscopic depth camera with inertial sensor for robotics." Stereolabs Developer Documentation. https://www.stereolabs.com/en-bg/store/products/zed-2i
[13] Ouster. (2025). "OS0 – Ultra-wide field of view LiDAR for close-range navigation and manipulation." Ouster Sensor Specifications. https://ouster.com/insights/blog/the-os0-wide-view-lidar-sensor-deep-dive
[14] G. Tian, I. Khan, Impact energy end-uses and efficiency indicators on the environmental polisy stringency index, IEEE, Energy 326 (2025) 136232, www.elsevier.com/locate/energy, https://doi.org/10.1016/j.energy.2025.136232.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Aleksandar Tsvetanov, Benislav Dimitrov, Desislava Petrova
This work is licensed under a Creative Commons Attribution 4.0 International License.
