Foundation Models for Autonomous Driving System: An Initial Roadmap

1. Problem Statement

Autonomous Driving Systems (ADS) operate in open, dynamic, and safety-critical environments. Traditional ADS approaches rely heavily on predetermined rules and supervised learning over finite, labeled datasets. These methods struggle with:

• Long-tail scenarios: Unseen situations, unexpected obstacles, and complex dynamic interactions.
• Generalization: Inability to fully capture the diversity of real-world driving conditions.
• Safety-Critical Integration: While Foundation Models (FMs)—including Large Language Models (LLMs), Vision-Language Models (VLMs), and World Models—offer unprecedented reasoning and generalization capabilities, their integration into ADS introduces substantial Software Engineering (SE) challenges. These include data curation, system design, deployment efficiency, evaluation, and assurance, which are not yet fully addressed by existing literature.

2. Methodology

The authors conducted a structured literature review to synthesize the current state of FM integration in ADS and propose a roadmap.

• Scope: January 2020 to January 2026, covering the emergence of large-scale pre-trained models (LLMs, VLMs, World Models).
• Data Sources: Primary search via the DBLP database using a comprehensive search string combining FM synonyms (e.g., "foundation model," "LLM," "VLM") and ADS synonyms (e.g., "autonomous driving," "self-driving").
• Selection Criteria:
  • Inclusion: Papers proposing/evaluating FMs for ADS, introducing relevant datasets/benchmarks, or discussing deployment/system issues (latency, safety).
  • Exclusion: Non-peer-reviewed papers (except for influential industrial technical reports), papers unrelated to ADS (e.g., general robotics), and papers lacking technical depth.
• Analytical Framework: The review is structured around three critical dimensions: FM Infrastructure, In-Vehicle Integration, and Practical Deployment. The authors trace dependencies from infrastructure decisions to vehicle risks and final practice failures (as illustrated in Table 1 of the paper).

3. Key Contributions

The paper provides the first structured roadmap for integrating FMs into ADS, categorized into three dimensions with specific challenges and opportunities:

A. FM Infrastructure (The Foundation)

• High-Quality Datasets:
  • Challenge: Bias in training data (demographic attributes) and privacy risks (memorization of PII).
  • Opportunity: Develop bias mitigation strategies that extend beyond perception to decision-making pipelines; implement Machine Unlearning to remove private data or biases from deployed models without full retraining.
• Computational Resources & Hardware:
  • Challenge: Reliance on "black-box" proprietary hardware (GPUs) creates security vulnerabilities (side-channel attacks, model theft) and lacks Trusted Execution Environments (TEEs) for high-bandwidth sensor data.
  • Opportunity: Design Security-by-Design hardware architectures with TEEs tailored for AI accelerators and promote open, verifiable hardware stacks.
• Development & Deployment Toolchains:
  • Challenge: Complexity of distributed training (e.g., Ray, DeepSpeed) and lack of QA tools for FM-specific bugs.
  • Opportunity: Empirical analysis of toolchain inefficiencies and development of robust-by-design optimization techniques (pruning, quantization) that co-design security with performance.

B. In-Vehicle Integration (The System)

• Enhancing Core Modules: FMs are applied to perception, decision-making, navigation, and end-to-end control.
• Challenge: Hallucination: FMs may generate factually incorrect or unsafe outputs (e.g., "ghost objects," illegal maneuvers).
  • Opportunity: Develop multi-modal grounding techniques to verify FM outputs against raw sensor data (LiDAR, Camera) and create standardized benchmarks for hallucination detection.
• Challenge: Multi-modality Adaptation: Current systems often rely on brittle, cascaded pipelines where perception errors cascade into planning failures.
  • Opportunity: Move toward End-to-End Multi-Modal Fusion models that ingest raw sensor data directly to learn robust internal representations.
• Domain-Specific Models: Most powerful FMs are proprietary.
  • Opportunity: Create an open-source, domain-specific foundation model for autonomous driving to democratize research.

C. Practical Deployment (The Application)

• Intelligent User Experience: FMs enable personalized driving (e.g., "drive aggressively") and surrounding awareness.
  • Challenge: Safety alignment and jailbreak susceptibility (users forcing unsafe behaviors).
  • Opportunity: Enhance Forward Alignment (human feedback during training) and Backward Alignment (safety benchmarks) to ensure compliance with traffic rules.
• Code Generation: Using LLMs to generate ADS code.
  • Challenge: LLMs may produce syntactically correct but semantically flawed or unsafe code.
  • Opportunity: Develop automated verification techniques specifically for FM-generated code in safety-critical contexts.
• Ethical & Moral Testing:
  • Opportunity: Establish moral testing pipelines using simulation-based metamorphic testing to evaluate decision-making in ethically sensitive scenarios.

4. Results and Roadmap Goals

The paper does not present experimental results of a new model but synthesizes the landscape to define a three-phase research roadmap:

1. Short-Term Goals (Foundational):
   • Establish unified data management frameworks and automated license compliance tools.
   • Improve development toolchains via empirical analysis and new QA techniques for distributed systems.
   • Conduct empirical studies on the impact of optimization (quantization/pruning) on model trustworthiness.
2. Mid-Term Goals (System Integration):
   • Develop End-to-End Multi-Modal Fusion models to reduce hallucination and perception dependency.
   • Architect Edge-Cloud Collaboration systems for intelligent task orchestration.
   • Release an Open-Source Domain-Specific Foundation Model for ADS.
3. Long-Term Goals (Trustworthiness at Scale):
   • Establish a Hardware Root of Trust (TEEs for AI accelerators).
   • Achieve robust AI Alignment (forward and backward) to ensure models adhere to human values and safety constraints.
   • Guarantee Code and System Correctness via formal verification of FM-generated code.

5. Significance

• Holistic Perspective: Unlike previous surveys that focus on specific model families or tasks (e.g., just perception or just testing), this paper provides a system-level view connecting infrastructure decisions to real-world safety failures.
• Software Engineering Focus: It uniquely frames FM integration challenges through the lens of Software Engineering, addressing data curation, toolchain reliability, and code verification, which are often overlooked in AI-centric surveys.
• Actionable Guidance: By categorizing challenges and opportunities into short, mid, and long-term goals, the paper serves as a strategic guide for researchers, practitioners, and policymakers to build reliable, safe, and trustworthy FM-enabled autonomous driving systems.
• Bridging the Gap: It highlights the critical need to move from "black-box" proprietary solutions to open, verifiable, and ethically aligned systems, addressing the "long-tail" problem that has stalled the deployment of Level 4/5 autonomy.