MobEvolve is an agentic self-evolving heuristic framework that generates interpretable human mobility trajectories and outperforms deep generative and LLM-based methods on Singapore and Montreal benchmarks.
Generative ai in transportation planning: A survey
4 Pith papers cite this work. Polarity classification is still indexing.
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A novel two-level Plackett-Luce model with Bayesian inference supports personalized route choice and preference modeling in smart mobility platforms.
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.
A schema-grounded natural language interface for transportation safety data that uses LLMs for intent interpretation, rule-based validation, and deterministic spatial query execution on an authoritative database.
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LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.