Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion
arXiv:2606.02979v1 Announce Type: cross Abstract: We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass. The model performs multiple views of semantic segmentation, depth estimation, light detection and ranging (LiDAR) segmentation, and bird's eye view projection simultaneously without being supported by other models. We also provide an adaptive loss weighting algorithm to tackle the imbalanced learning issue that occurred due to plenty of given tasks. Through data pre-processing and intermediate sensor fusion techni
Advances in multi-task learning and sensor fusion are maturing, enabling more compact and efficient AI models for complex real-time applications like autonomous driving.
This development indicates a significant step towards more computationally efficient and performant autonomous driving systems, which directly impacts their commercial viability and widespread adoption.
Autonomous driving perception systems can now process multiple sensor inputs and perform various tasks simultaneously with a single, compact model, reducing computational overhead and potentially improving real-time decision-making.
- · Autonomous vehicle manufacturers
- · GPU and edge AI chip developers
- · Logistics and transportation sectors
- · AI model optimization software providers
- · Developers of less efficient, single-task AI models
- · Cloud-based autonomous driving solutions (for certain tasks)
More compact and efficient autonomous driving AI models will accelerate development and reduce hardware costs.
Reduced computational requirements could lead to faster deployment of Level 4/5 autonomous systems in diverse environments.
This efficiency might open pathways for autonomous systems in smaller, cost-sensitive vehicles or specialized robotics beyond traditional automotive applications.
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Read at arXiv cs.AI