Multimodal Machine Learning-Based Unmanned Driving System

DOI: https://doi.org/10.62517/jike.202604224

Author(s)

Jiayang Liang*, Shuo Zhao

Affiliation(s)

Electronic and Automation College, City Institute, Dalian University of Technology, Dalian, Liaoning, China *Corresponding Author

Abstract

With the breakthrough of artificial intelligence technology, unmanned driving system are moving from theory to practical application. However, they face multiple disturbances such as sensor anomalies and extreme weather in real dynamic and complex environments, posing severe challenges to the safe and reliable operation of the systems. Although machine learning technology has significantly improved system performance, it still has insufficient robustness when dealing with these challenges. Therefore, in-depth research and improvement of system robustness are crucial for ensuring driving safety and facilitating the application of the technology. This paper focuses on machine learning-based unmanned driving systems and conducts a systematic study of their robustness issues. The study begins by sorting out the core robustness challenges that the system faces at the levels of perception, decision-making, and control. Based on this, this paper explores comprehensive robustness enhancement methods including data augmentation, adversarial training, regularization, redundant design, and high-fidelity simulation testing from two dimensions: internal enhancement of machine learning algorithms and external assurance of system architecture. The findings suggest that enhancing the robustness of unmanned driving system is a systematic project that requires a combination of algorithm optimization, architectural fault tolerance, and complete verification. Significant progress has been made through the combination of multiple levels of technology at present, but in the future, there is a need to further evolve from "robustness" in response to known threats to "system resilience" with adaptive capabilities. Finally, closing the verification gap between simulation and reality and achieving collaborative optimization of multiple technical paths are key directions for building truly safe and reliable unmanned driving system.

Keywords

Machine Learning; Driverless Technology; Robustness Research

References

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