Multimodal Large Language Models for Autonomous Driving: A Comprehensive Survey of Perception, Reasoning, Planning, and Safety Assurance
TL;DR
This 2026 survey synthesises 39 primary sources selected from an initial corpus of 274 candidate records screened over 2020-2026, organises the field around a five-role pipeline taxonomy (perception, prediction, planning, control, and human-machine interaction), and compares six representative driving MLLMs on accuracy, latency, and parameter footprint.
Abstract
Autonomous driving has progressed from rule-based subsystems and modular perception, prediction, and planning stacks toward unified data-driven architectures, and multimodal large language models (MLLMs) are increasingly proposed as the cognitive substrate of the next generation of highly automated road vehicles. This 2026 survey synthesises 39 primary sources selected from an initial corpus of 274 candidate records screened over 2020-2026, organises the field around a five-role pipeline taxonomy (perception, prediction, planning, control, and human-machine interaction), and compares six representative driving MLLMs (DriveGPT-4, LMDrive, Senna, DriveLM, GPT-4V-AD, and Cosmos-1) on accuracy, latency, and parameter footprint. A benchmark coverage matrix over LingoQA, BDD-X, DriveLM, nuScenes-QA, AutoHallu, and CODA-LM exposes evaluation gaps in prediction and planning. Model behaviour is translated into safety-assurance terms by mapping four MLLM failure-mode families to the functional-safety standard ISO 26262, the Safety of the Intended Functionality standard ISO 21448 (SOTIF), and the autonomous-systems safety-case standard UL 4600. A three-tier vehicle, edge, and cloud deployment topology is described together with the digital-twin and over-the-air update infrastructure that surrounds it. The strongest empirical finding is that Cosmos-1 delivers the best accuracy among models with sub-150 ms latency (76.6 percent mean reasoning accuracy at 480 ms), leaving verifiable safety certification as the single most important open problem for closed-loop deployment. The survey closes with a six-item research agenda spanning sub-100 ms real-time inference, out-of-distribution generalisation, multi-agent intent reasoning, verifiable safety certification, long-tail corner-case coverage, and closed-loop sim-to-real transfer. The article is intended as a reference for automotive system architects, safety engineers, regulators, and machine-learning researchers preparing the next generation of automated driving systems.