arXiv:2308.14329v4 Announce Type: replace-cross Abstract: In autonomous driving, the end-to-end (E2E) driving approach that predicts vehicle control signals directly from sensor data is rapidly gaining attention. To learn a safe E2E driving system, one needs an extensive amount of driving data and human intervention. Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets. Often, publicly available driving datasets are collected with limited driving scenes, and collecting vehicle control data is only available by veh
The accelerating pace of AI development and the practical challenges of data collection for autonomous driving are pushing research towards more efficient learning paradigms.
Self-supervised imitation learning could significantly reduce the cost and complexity of developing safe and effective end-to-end autonomous driving systems, accelerating deployment.
The reliance on extensive, manually curated datasets for autonomous vehicle training could be mitigated, potentially lowering barriers to entry and speeding up development cycles.
- · Autonomous vehicle developers
- · AI software companies
- · Logistics and transportation sectors
- · Companies reliant on traditional, labor-intensive data collection for AVs
More rapid and cost-effective development of AI systems for autonomous driving becomes possible.
Increased adoption and deployment of autonomous vehicles may occur due to improved safety and reduced development costs.
The broader application of self-supervised imitation learning could extend to other complex robotic control tasks, accelerating automation across industries.
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Read at arXiv cs.AI