LLM-Driven Scenario-Aware Planning for Autonomous Driving

• URL: http://arxiv.org/abs/2601.21876v1
• Date: Thu, 29 Jan 2026 15:42:13 GMT
• Title: LLM-Driven Scenario-Aware Planning for Autonomous Driving
• Abstract summary: Hybrid planner switching framework (HPSF) for autonomous driving needs to reconcile high-speed driving efficiency with safe maneuvering in dense traffic.<n>Existing HPSF methods often fail to make reliable mode transitions or sustain efficient driving in congested environments.<n>This paper proposes a large language model (LLM) driven, adaptive planning method, which switches between high-speed driving in low-complexity scenes and precise driving in high-complexity scenes.
• Score: 35.75515733294211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid planner switching framework (HPSF) for autonomous driving needs to reconcile high-speed driving efficiency with safe maneuvering in dense traffic. Existing HPSF methods often fail to make reliable mode transitions or sustain efficient driving in congested environments, owing to heuristic scene recognition and low-frequency control updates. To address the limitation, this paper proposes LAP, a large language model (LLM) driven, adaptive planning method, which switches between high-speed driving in low-complexity scenes and precise driving in high-complexity scenes, enabling high qualities of trajectory generation through confined gaps. This is achieved by leveraging LLM for scene understanding and integrating its inference into the joint optimization of mode configuration and motion planning. The joint optimization is solved using tree-search model predictive control and alternating minimization. We implement LAP by Python in Robot Operating System (ROS). High-fidelity simulation results show that the proposed LAP outperforms other benchmarks in terms of both driving time and success rate.

Related papers

• A Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving [96.81243316566764]
  Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving.<n>We propose a robust VLA-based end-to-end autonomous driving system that combines multi-modality interaction with multi-trajectory planning and optimization.
  arXiv  Detail & Related papers  (2026-08-21T09:06:16Z)
• World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning [60.88859946006306]
  Adversa World Modeling is a theoretically grounded multi-agent self-play fine-tuning framework.<n>In inner minimization, the planner's predictive world model is converted into a role-conditioned adversary that learns sparse, scene-adaptive attack coalitions.<n> Experiments on the nuPlan and InterPlan benchmarks demonstrate that our method generates transferable adversarial interactions.
  arXiv  Detail & Related papers  (2026-07-12T07:44:16Z)
• AdaDrive: Self-Adaptive Slow-Fast System for Language-Grounded Autonomous Driving [71.55254573283793]
  Existing approaches either activate Large Language Models too frequently, causing excessive computational overhead, or use fixed schedules.<n>We propose AdaDrive, an adaptively collaborative slow-fast framework that optimally determines when and how LLMs contribute to decision-making.<n>AdaDrive provides a flexible, context-aware framework that maximizes decision accuracy without compromising real-time performance.
  arXiv  Detail & Related papers  (2025-11-09T07:05:03Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control [10.9431172488753]
  We introduce a parallel hybrid action space reinforcement learning model (PH-DDPG) that optimize traffic signal phase and duration of traffic signals simultaneously.<n>Our model features a task-specific parallel hybrid action space tailored for adaptive traffic control, which directly outputs discrete phase selections and their associated continuous duration parameters concurrently.
  arXiv  Detail & Related papers  (2025-03-18T13:38:53Z)
• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy.<n>A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• From Imitation to Exploration: End-to-end Autonomous Driving based on World Model [24.578178308010912]
  RAMBLE is an end-to-end world model-based RL method for driving decision-making.<n>It can handle complex and dynamic traffic scenarios.<n>It achieves state-of-the-art performance in route completion rate on the CARLA Leaderboard 1.0 and completes all 38 scenarios on the CARLA Leaderboard 2.0.
  arXiv  Detail & Related papers  (2024-10-03T06:45:59Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic [0.9281671380673306]
  This paper presents a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting trajectory. Our approach runs in real-time using a custom GPU-accelerated batch, and a Variational Autoencoder learnt warm-start strategy. Our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
  arXiv  Detail & Related papers  (2022-12-05T12:56:42Z)
• A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning [23.007323699176467]
  We propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers. Safety is guaranteed by the low-level optimization/sampling-based controllers, while the high-level reinforcement learning algorithm makes H-CtRL an adaptive and efficient behavior planner. The proposed H-CtRL is proved to be effective in various realistic simulation scenarios, with satisfying performance in terms of both safety and efficiency.
  arXiv  Detail & Related papers  (2021-01-17T20:45:42Z)
• Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning [21.500697097095408]
  Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost. We propose a Hierarchical Reinforcement Learning structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning.
  arXiv  Detail & Related papers  (2020-11-09T20:49:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.