Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving
arXiv:2606.20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories. Existing paradigms face a distinct dichotomy between representational efficiency and generalization capacity. Dense models (e.g., occupancy networks), while geometrically robust, incur critical computational bottlenecks and struggle with high-level semantic reasoning. Conversely, sparse, query-based planners are efficient but reliant on closed-set de
The continuous push for more robust and generalizable autonomous driving systems in increasingly complex environments necessitate new approaches to overcome current limitations.
This development represents a significant step towards enabling autonomous vehicles to navigate highly unpredictable real-world scenarios, crucial for broad adoption and safety.
The explicit addressing of the dichotomy between representational efficiency and generalization capacity through a novel energy-based sparse framework offers a path to more reliable end-to-end driving systems.
- · Autonomous vehicle developers
- · Logistics and transportation industries
- · AI researchers in robotics
- · Developers focused solely on dense perceptual models
- · Traditional closed-set planning systems
- · Companies unable to integrate advanced AI models
More resilient and adaptable autonomous driving systems emerge, reducing intervention rates.
Accelerated deployment of autonomous vehicles in urban and less-structured environments becomes feasible.
Reduced accident rates and increased efficiency in transportation could lead to new economic models and urban planning strategies.
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Read at arXiv cs.AI