Enterprise AI Analysis
Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization
Autonomous Vehicles (AVs) frequently immobilize in complex traffic scenarios where human drivers excel. Traditional recovery methods, such as remote intervention and manual takeover, are often costly, inefficient, and inaccessible to non-driving passengers. This paper introduces StuckSolver, a novel LLM-driven framework designed to enable AVs to autonomously resolve immobilization or recover with passenger guidance.
Executive Impact & ROI
StuckSolver significantly enhances AV resilience and accessibility by providing an efficient, LLM-powered recovery mechanism. This reduces operational costs associated with remote interventions and expands AV services to a broader user base, including non-drivers.
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Driving Score Achieved
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Deep Analysis & Enterprise Applications
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StuckSolver: An LLM-Driven Recovery Architecture
StuckSolver is designed as a plug-in add-on module operating on top of existing AV perception-planning-control stacks. It leverages Large Language Models (LLMs) with Chain-of-Thought (CoT) reasoning and function calling for multimodal information processing, enabling self-recovery or passenger-guided decision-making in immobilization scenarios. It continuously monitors AV status and intervenes when necessary by generating high-level recovery commands.
Enterprise Process Flow
S1. Observation
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S2. Analysis
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S3. Decision-making
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Recovery Plan Output
Seamless Integration & Addressing Current Gaps
StuckSolver integrates non-intrusively into AV systems, only intervening when immobilization is detected. It communicates via structured APIs with perception, route planning, and decision-making modules. This design overcomes limitations of traditional methods like costly remote control or restricted manual takeover, providing a robust and accessible solution for AV recovery.
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Validated Performance & Real-World Scenarios
Evaluated on the Bench2Drive benchmark and custom uncertainty scenarios, StuckSolver demonstrates near-state-of-the-art performance. It significantly improves Driving Score and Success Rate autonomously, with further enhancements when passenger guidance is incorporated, validating its effectiveness in complex traffic situations.
70.89 DS
Max Driving Score Achieved with Passenger Guidance
Case Study: Scenario - Vehicle with Open Door
Description: An autonomous vehicle encountered a stationary white sedan in the adjacent right lane with its left door fully open, posing a potential collision risk and causing the AV to immobilize.
StuckSolver's Action: StuckSolver analyzed the situation, correctly identified the immobilization cause, and generated a new behavior plan to perform a safe left lane change, bypassing the obstruction.
Outcome: The AV successfully avoided the collision and resumed normal travel without human intervention, demonstrating StuckSolver's ability to handle complex unforeseen obstacles autonomously and maintain operational continuity.
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Projected Annual Savings
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Your AI Implementation Roadmap
Embark on a structured journey to integrate AI successfully into your enterprise. Our proven methodology guides you from foundational strategy to advanced deployment and continuous optimization.
Phase 1: Discovery & Strategy
We begin with a comprehensive audit of your current operations, identifying key immobilization scenarios and potential for AI integration. This phase includes defining project scope, setting clear objectives, and developing a tailored AI strategy that aligns with your enterprise goals.
Phase 2: Pilot & Integration
In this phase, we deploy StuckSolver as a plug-in module within a controlled pilot environment. We focus on seamless integration with your existing AV stack, rigorous testing in simulated and real-world scenarios, and initial performance validation. Key metrics will be tracked to ensure successful adoption.
Phase 3: Scale & Optimize
Upon successful pilot, we scale the StuckSolver solution across your AV fleet. This phase includes ongoing monitoring, performance optimization, and continuous refinement based on operational data and evolving traffic conditions. We also explore opportunities for further enhancements like LLM distillation for faster inference.
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