arXiv:2603.14354v3 Announce Type: replace Abstract: End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents. To address these issues, we propose DeLL, a Deconfounded Lifelong Learning framework that integrates a Dirichlet process mixture model (DPMM) with the front-door adjustment mechanism from causal inference. The DPMM is employed to construct two dynamic knowledge spaces: a trajectory k
The proliferation of complex AI systems, particularly in critical applications like autonomous driving, necessitates robust lifelong learning mechanisms to adapt to new scenarios and mitigate catastrophic forgetting.
This research addresses fundamental limitations in current autonomous driving AI, enhancing their safety, reliability, and ability to learn continuously without compromising previously acquired knowledge.
The proposed 'DeLL' framework introduces a causal inference approach to lifelong learning, which could lead to more robust and adaptable autonomous systems that better handle real-world complexities.
- · Autonomous Driving Companies
- · AI/ML Researchers
- · Robotics Industry
- · Supply Chain & Logistics
- · Traditional AI Development
- · Companies with Static AI Models
Improvements in lifelong learning will accelerate the development and deployment of safe and effective autonomous vehicles.
The causal inference approach could be generalized to other AI applications, leading to more resilient and explainable AI systems across various industries.
Enhanced AI robustness might enable faster regulatory approval and wider public acceptance of autonomous technologies, transforming transportation and logistics ecosystems.
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Read at arXiv cs.LG