AUTODRIVER: Automated Driver Frameworks

• AUTODRIVER is a suite of automated systems that integrate LLM-driven kernel driver maintenance, autonomous vehicle control, digital twin simulation, and driver identification from sensor data.
• It employs closed-loop, multi-agent frameworks with static analysis and iterative validation to ensure reliable performance and safety across diverse applications.
• Applications span kernel driver updates, urban AV systems, and sim2real digital twin platforms, with robust evaluation metrics and ongoing improvements to address hardware and generalization challenges.

AUTODRIVER refers to a class of automated systems—spanning both the domain of operating system driver maintenance and intelligent vehicular/autonomous driving—characterized by autonomy, closed-loop control, multi-stage agent orchestration, and a reliance on data-driven or reasoning-based models. The term appears in several distinct research contexts, most notably: (1) LLM-driven automation of Linux kernel driver maintenance, (2) autonomous driving agents (urban, highway, or simulated environments), (3) digital twin and sim2real research platforms, and (4) privacy-focused automated driver identification from automotive data. Definitions, architecture, technical capabilities, evaluation strategies, and future directions vary by domain, but all AUTODRIVER systems share the goal of reducing human-in-the-loop maintenance or decision-making by introducing adaptive, robust automation frameworks.

1. LLM-Driven Kernel Driver Maintenance: AutoDriver

The AutoDriver system, as developed in "LLM-Driven Kernel Evolution: Automating Driver Updates in Linux" (Kharlamova et al., 24 Nov 2025), is a closed-loop, multi-agent framework designed to automate the maintenance of device drivers in the Linux kernel ecosystem as API/ABI changes, semantic shifts, and security hardenings break compatibility across kernel versions. At its core, AutoDriver integrates four major components:

• Prompt Engineering Module: Constructs taxonomy-aware prompts by merging relevant code snippets (pre/post-update), semantic category labels (e.g., "API migration"), and commit metadata to focus the LLM (e.g., ChatGPT, DeepSeek) on the minimal code hunk requiring updates.
• Multi-Agent Collaboration: Four cooperating agents—Prompt Engineering, Coding, Patch Fix, and Static Analysis—communicate via structured JSON blocks, synthesizing, repairing, and vetting candidate patches via iterative error feedback.
• Static Analysis Engine: Enforces syntactic and structural constraints using AST-similarity metrics. The composite static score, a weighted sum of AST similarity, function accuracy, call accuracy, node accuracy, and variable accuracy, filters out functionally or semantically inconsistent patches. Only patches with $S_{\mathrm{static}} \geq 0.7$ proceed to dynamic validation.
• Iterative Validation Loop: Candidate patches are built and tested in a staged containerized environment. Compilation errors are iteratively fed back to the Coding Agent for up to $N=5$–$10$ correction cycles. Successful patches are loaded into QEMU for driver boot, module load/unload verification, and trace comparison to ensure functional parity.

The system achieves a 56.4% compilation success rate on 55 held-out cases; of these, 90% pass QEMU-based dynamic validation, signaling successful driver initialization and module unload. The system's static metrics demonstrate higher discriminative capability with DeepSeek versus ChatGPT backends (e.g., AST similarity: 0.912 vs. 0.889). Identified limitations include the challenge of cross-file, cross-subsystem migrations, incomplete hardware-accurate emulation, and the need for prompt and API-localization improvements (Kharlamova et al., 24 Nov 2025).

2. AUTODRIVER in Autonomous Vehicle Technology

Multiple research programs and open platforms adopt the term AUTODRIVER to denote end-to-end or modular autonomous driving stacks across real and simulated vehicles.

Urban/Research AV Systems

A canonical urban AUTODRIVER system (Cosgun et al., 2017) employs a multilayered architecture:

• Sensors & Map (multi-camera, LiDAR, radar, GPS/INS, V2X);
• Perception (localization, object detection/tracking, data fusion via EKF, particle filters);
• Decision (hierarchical event-driven FSM, route and behavior planning, trajectory generation);
• Actuation (proprietary steering/brake/throttle controllers).

The system demonstrates robust intersection handling, pedestrian avoidance (using joint vision-LiDAR and V2P), and construction zone navigation using an obstacle-aware cost function $J_p$, with empirical validation spanning 44 urban runs and three human interventions (primarily GPS failures). Notable limitations include the requirement of high-fidelity maps, FSM rigidity, and limited generalization to unstructured public roads (Cosgun et al., 2017).

Digital Twin and Research Platforms

AutoDRIVE (Samak et al., 2022, Samak et al., 2022) designates an integrated digital-twin ecosystem incorporating a real-world 1:14 scale car (Nigel), photorealistic simulator (Unity+PhysX), and a comprehensive modular infrastructure (terrain, traffic, V2I elements). It supports software/hardware-in-the-loop workflows, single/multi-agent RL and DL pipelines (including behavioral cloning and PPO-based intersection traversal), and smart city management using distributed V2V/V2I protocols. Core stack components include occupancy SLAM (Hector), A*/TEB planning, adaptive controllers, and REST/ROS/web API integration.

Demonstrated use-cases include autonomous parking (SLAM, global+local planning), sim2real behavioral cloning, distributed intersection negotiation with V2V/PPO, and city-scale traffic infrastructure simulation. Performance validation uses both domain-specific and standard RL/DIL metrics. The system emphasizes scalability, reproducibility, and rapid prototyping (Samak et al., 2022, Samak et al., 2022).

3. AutoDriver for Driver Identification and Profiling

The term AUTODRIVER also appears in the context of attribute-based automatic driver identification from in-vehicle sensor or CAN-bus data (Remeli et al., 2019, Yang et al., 2021). Two representative systems:

• Byte-Stream Identification Model (Remeli et al., 2019): Uses per-byte time-series extracted from CAN-IDs, processed by per-ITS convolutional-LSTM-attention models, merged via an expert committee. Achieves 75–85% accuracy in 1-vs-all reidentification from 20–120 second windows even without explicit signal decoding.
• Driver2vec Architecture (Yang et al., 2021): Extracts a 62-dimensional driver behavior embedding using parallel TCN and Haar-wavelet branches, discriminatively trained with triplet loss, followed by GBDT classification. Pairwise identification accuracy exceeds 83% over 51 drivers in 10 s snippets.

These approaches underline the privacy and security risks inherent to raw vehicular telemetry, demonstrating that fine-grained driver fingerprints are learnable directly from low-level telemetry.

4. Model Classes: End-to-End, Deep Generative, and Reasoning-Based AUTODRIVERs

End-to-End Deep Imitation and RL

Recent end-to-end AUTODRIVER approaches incorporate deep RL and imitation learning for direct mapping from sensory environments to low-level actuation. For example, SGADS (Tang et al., 22 Jan 2024) employs a latent world-model with normalizing flows to achieve robust safety-constrained RL and rapid sim2real adaptation; GAD (Sun et al., 1 May 2024) fuses perception with GAN-based trajectory generation/planning, supporting production-scale HD-map-free driving by training on human demonstration data. Both systems explicitly address sample efficiency, safety via time-to-collision and steering constraints, and generalization across urban and rural topographies.

Visual-LLM Driven Assistants

VLM-Auto (Guo et al., 9 May 2024) introduces a Visual LLM (VLM, Qwen-VL) to bridge high-level scene understanding (weather, lighting, safety) with behavior-tree driving policy generation, yielding smoother control and robust adaptation (96–97% precision in night/gloomy conditions). The modular prompt/behavior mapping design decouples perception from control, minimizing hallucinations and enhancing interpretability.

World Model and Diffusion-Based AUTODRIVERs

ADriver-I (Jia et al., 2023) presents a conceptually unified AUTODRIVER framework combining an interleaved vision-action token representation, multimodal LLM control policy, and a video diffusion model to autoregressively simulate both control and visual environments indefinitely. This fully model-based loop enables closed-world synthetic rollouts and serves as a generative backbone for downstream RL or planning, although currently lacks joint optimization for decision-generation alignment.

Rule-Based and Commonsense Reasoning

AUTO-DISCERN (Kothawade et al., 2021) integrates deep-learning-based perception with an s(CASP)-driven answer-set programming (ASP) core encoding 35 commonsense driving and ethical rules. The ASP decision engine guarantees hard safety constraints (e.g., never allow a non-braking action near a pedestrian), supports explainable, provable action-selection, and achieves real-time performance (0.5s per decision).

5. Evaluation, Validation Protocols, and Automated Testing

Empirical benchmarks for AUTODRIVER systems span simulated and real environments, with extensive use of performance KPIs, scenario coverage, and statistical metrics:

• Kernel Driver Maintenance (Kharlamova et al., 24 Nov 2025): Measured by compilation success and QEMU-validated dynamic behavior, stratified by taxonomy class and LLM backend.
• Urban AV and Digital Twin Deployments (Samak et al., 2022, Cosgun et al., 2017): Validated via intervention rates, scenario completion, cross-track errors, SLAM map accuracy, RL reward curves, and sim2real transfer success.
• Driver Fingerprinting (Remeli et al., 2019, Yang et al., 2021): Identification accuracy in multi-way, balanced/unbalanced, and "none of the above" settings, area-specific breakdown, and real-time inference latency.
• Open-Source Evaluation Toolkits (Becker et al., 24 Jun 2024): Scenario parameterization via OpenDRIVE/XODR+, automated coverage of road geometry spaces (e.g., lane width, curvature, intersection angle), with KPIs spanning lateral deviation, time-to-collision, jerk, comfort measures, and success rates aggregated over hundreds to thousands of parameterized test runs.

Across approaches, precise quantitative and scenario-based testing enables rapid surfacing of model weaknesses (e.g., critical radius for curve negotiation, hallucination rates under perceptual uncertainty) and incremental controller refinement.

6. Current Limitations and Anticipated Developments

Documented limitations include:

• Multi-file and multi-subsystem update in driver maintenance (LLMs degrade sharply on edits intersecting several headers/sources; lack of external symbol retrieval and graph localization remains a bottleneck) (Kharlamova et al., 24 Nov 2025).
• Hardware-specific fidelity in AV emulation and road scenarios (e.g., QEMU lacks full peripheral register/timing emulation, real-world traffic and sensor uncertainties are imperfectly recapitulated in current simulators) (Cosgun et al., 2017, Kharlamova et al., 24 Nov 2025).
• Data coverage and generalization boundaries (fingerprinting results are shown only for a single car make, limited urban scenarios, or a modest set of ODD parameters) (Remeli et al., 2019, Becker et al., 24 Jun 2024).
• Decoupled world-model architectures in generative LLM/diffusion pipelines (no joint adaptation of vision-action prediction and future-state synthesis) (Jia et al., 2023).
• Rigidity in rule-based/ASP reasoning when facing distribution shift or high-dimensional perceptual errors; future plans involve temporal-ASP extensions and sensor-fusion integrity layers (Kothawade et al., 2021).

Planned extensions involve: deep retrieval and API-change graphing for kernel driver update localization, symbolic execution for driver hardware validation, end-to-end joint optimization in world-model architectures, richer sim2real adaptation workflows, and increasingly formalized safety/comfort assessment frameworks for scenario validation.

---

In sum, AUTODRIVER denotes a suite of advanced, closed-loop, automation frameworks across systems software and vehicular intelligence. Whether orchestrating driver adaptation in the evolving Linux kernel, synthesizing interpretable driving agents or digital twins, fingerprinting human operators, or advancing commonsense symbolic reasoning in AVs, research continues to integrate data-driven, generative, and rule-based disciplines to close the autonomy gap and robustly validate function against evolving requirements and environments.