LLM4AD: Large Language Models for Autonomous Driving - Concept, Review, Benchmark, Experiments, and Future Trends

arXiv — cs.CLThursday, November 13, 2025 at 5:00:00 AM
The recent publication titled 'LLM4AD: Large Language Models for Autonomous Driving' marks a significant advancement in the integration of artificial intelligence with autonomous driving technology. By introducing the concept of LLM4AD, the authors highlight the potential of large language models to enhance various aspects of autonomous driving, including perception and decision-making. The paper not only reviews existing studies but also proposes a comprehensive benchmark for evaluating these systems, which includes innovative tools like LaMPilot-Bench and the CARLA Leaderboard. Extensive real-world experiments conducted on autonomous vehicle platforms further validate the practical applications of LLMs in this field. The exploration of future trends, particularly the integration of language diffusion models, underscores the ongoing evolution of this technology. However, the research also addresses critical challenges such as latency, security, and personalization, which must be overc…
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Fair In-Context Learning via Latent Concept Variables
PositiveArtificial Intelligence
The paper titled 'Fair In-Context Learning via Latent Concept Variables' explores the in-context learning (ICL) capabilities of large language models (LLMs) in handling tabular data. It highlights the potential for LLMs to inherit biases from pre-training data, which can lead to discrimination in high-stakes applications. The authors propose an optimal demonstration selection method using latent concept variables to enhance task adaptation and fairness, alongside data augmentation strategies to minimize correlations between sensitive variables and predictive outcomes.
MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding
PositiveArtificial Intelligence
MMEdge is a proposed framework aimed at enhancing real-time multimodal inference on resource-constrained edge devices, crucial for applications like autonomous driving and mobile health. It addresses the challenges of sensing dynamics and model execution by decomposing the inference process into fine-grained units, allowing incremental computation as data arrives. Additionally, a lightweight temporal aggregation module is introduced to capture temporal dynamics, ensuring accuracy across different modalities.
Silenced Biases: The Dark Side LLMs Learned to Refuse
NegativeArtificial Intelligence
Safety-aligned large language models (LLMs) are increasingly used in sensitive applications where fairness is crucial. Evaluating their fairness is complex, often relying on standard question-answer methods that misinterpret refusal responses as indicators of fairness. This paper introduces the concept of silenced biases, which are unfair preferences hidden within the models' latent space, masked by safety-alignment. Previous methods have limitations, prompting the need for new approaches to uncover these biases effectively.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
NeutralArtificial Intelligence
The article discusses the growing interest in world models, particularly in the context of advancements in multimodal large language models like GPT-4 and video generation models such as Sora. It provides a comprehensive review of the literature on world models, which serve to either understand the current state of the world or predict future dynamics. The review categorizes world models based on their functions: constructing internal representations and predicting future states, with applications in generative games, autonomous driving, robotics, and social simulacra.
Who Gets the Reward, Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
PositiveArtificial Intelligence
The article discusses a new theoretical framework for training multi-agent systems using large language models (LLMs). It aims to connect system-level evaluations with agent-level learning by integrating cooperative game-theoretic attribution and process reward modeling. This approach produces local, signed, and credit-conserving signals, enhancing cooperation among agents while penalizing harmful actions in failure scenarios.
Identifying and Analyzing Performance-Critical Tokens in Large Language Models
NeutralArtificial Intelligence
The paper titled 'Identifying and Analyzing Performance-Critical Tokens in Large Language Models' explores how large language models (LLMs) utilize in-context learning (ICL) for few-shot learning. It categorizes tokens in ICL prompts into content, stopword, and template tokens, aiming to identify those that significantly impact LLM performance. The study reveals that template and stopword tokens have a greater influence on performance than informative content tokens, challenging existing assumptions about human attention to informative words.
Modeling and Predicting Multi-Turn Answer Instability in Large Language Models
NeutralArtificial Intelligence
The paper titled 'Modeling and Predicting Multi-Turn Answer Instability in Large Language Models' discusses the evaluation of large language models (LLMs) in terms of their robustness during user interactions. The study employs multi-turn follow-up prompts to assess changes in model answers and accuracy dynamics using Markov chains. Results indicate vulnerabilities in LLMs, with a 10% accuracy drop for Gemini 1.5 Flash after a 'Think again' prompt over nine turns, and a 7.5% drop for Claude 3.5 Haiku with a reworded question. The findings suggest that accuracy can be modeled over time.
A Multifaceted Analysis of Negative Bias in Large Language Models through the Lens of Parametric Knowledge
NeutralArtificial Intelligence
A recent study published on arXiv examines the phenomenon of negative bias in large language models (LLMs), which refers to their tendency to generate negative responses in binary decision tasks. The research highlights that previous studies have primarily focused on identifying negative attention heads that contribute to this bias. The authors introduce a new evaluation pipeline that categorizes responses based on the model's parametric knowledge, revealing that the format of prompts significantly influences the responses more than the semantics of the content itself.