arXiv:2606.17082v1 Announce Type: cross Abstract: End-to-end autonomous parking has emerged as a critical task within the realm of autonomous driving. However, existing methods suffer from black-box characteristics, lacking high-level semantic understanding and interpretability, which impedes the realization of seamless long-distance autonomous parking from the road to the target spot. To address these limitations, we propose ParkingTransformer, a novel framework that leverages multi-view perception and the scene understanding capability of Large Language Models (LLMs). By combining trajectory
The convergence of advanced multi-view perception technologies and increasingly sophisticated Large Language Models (LLMs) makes this integration feasible and impactful for complex real-world tasks like autonomous parking.
This development addresses a critical limitation in autonomous driving by enhancing interpretability and semantic understanding, paving the way for more reliable and capable end-to-end autonomous systems.
Autonomous parking systems can now move beyond 'black-box' operations to incorporate high-level semantic reasoning, allowing for more seamless and context-aware maneuvering.
- · Autonomous vehicle manufacturers
- · Developers of LLMs for specialized applications
- · Urban planning and smart city initiatives
- · Consumers of autonomous driving technology
- · Traditional rule-based autonomous parking systems
- · Developers of less-interpretable autonomous driving solutions
Further acceleration in the development and deployment of L4/L5 autonomous driving features, particularly in urban environments.
Increased demand for robust, explainable AI architectures within safety-critical applications, pushing innovation in model interpretability.
Enhanced efficiency in parking infrastructure utilization and reduced traffic congestion due to optimized autonomous parking operations at scale.
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Read at arXiv cs.AI