Efficient Autonomy: Autonomous Driving of Retrofitted Electric Vehicles via Enhanced Transformer Modeling

In low-risk and open environments, such as farms and mining sites, efficient cargo transportation is essential. Despite the suitability of autonomous driving for these environments, its high deployment and maintenance costs limit large-scale adoption. To address this issue, a modular unmanned ground vehicle (UGV) system is proposed, which is adapted from existing platforms and supports both autonomous and manual control modes. The autonomous mode uses environmental perception and trajectory planning algorithms for efficient transport in structured scenarios, while the manual mode allows human oversight and flexible task management. To mitigate the control latency and execution delays caused by platform modifications, an enhanced transformer-based general dynamics model is introduced. Specifically, the model is trained on a custom-built dataset and optimized within a bicycle kinematic framework to improve control accuracy and system stability. In road tests allowing a positional error of up to 0.5 m, the transformer-based trajectory estimation method achieved 94.8% accuracy, significantly outperforming non-transformer baselines (54.6%). Notably, the test vehicle successfully passed all functional validations in autonomous driving trials, demonstrating the system’s reliability and robustness. The above results demonstrate the system’s stability and cost-effectiveness, providing a potential solution for scalable deployment of autonomous transport in low-risk environments.