arXiv:2508.00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments. Moreover, through vehicle-to-everything (V2X) communication, cooperative perception and driving among CAVs can be enabled, thereby mitigating the limitations of individual vehicles, while it also introduces stringent latency, reliability, and bandwidth constraints. Traditionally, tasks are addressed using separate models, which leads to hig
The increasing complexity of autonomous driving tasks and the data-intensive nature of V2X communication are driving the need for more efficient and robust AI architectures like deep multi-task learning.
This survey highlights how deep multi-task learning can optimize resource use and improve performance in connected autonomous vehicles, which is critical for their widespread adoption and safety.
The shift from separate models to integrated multi-task learning approaches will lead to more efficient, reliable, and secure autonomous driving systems, impacting vehicle design and regulatory frameworks.
- · Autonomous vehicle manufacturers
- · AI/ML research institutions
- · Semiconductor companies (edge AI hardware)
- · Smart city infrastructure developers
- · Companies relying on single-task AI models for AVs
- · Cloud-dependent AI solutions for real-time AV tasks
Improved safety and efficiency in autonomous vehicle operation through integrated AI systems.
Accelerated development and deployment of L4/L5 autonomous vehicle capabilities and V2X communication.
New standards and regulations for networked, multi-task AI systems in urban mobility, potentially influencing city planning and data governance.
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Read at arXiv cs.LG