Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition

arXiv — cs.CV•Wednesday, November 19, 2025 at 5:00:00 AM

- The introduction of TLS-Assist enhances LLM-based autonomous driving by integrating a modular traffic light and sign recognition system, addressing the challenges of detecting safety-critical objects. This development is crucial as it improves the reliability of autonomous vehicles, ensuring they adhere to traffic rules and respond accurately to their environment. The broader implications of this advancement highlight ongoing efforts to refine LLMs in safety-critical applications, emphasizing the need for robust mechanisms to mitigate risks associated with autonomous decision-making.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Recommended Readings

arXiv — cs.CL19 hours ago

ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

PositiveArtificial Intelligence

The introduction of ATLAS (AGI-Oriented Testbed for Logical Application in Science) marks a significant advancement in evaluating Large Language Models (LLMs). This new benchmark addresses the limitations of existing high-difficulty assessments, which often lack interdisciplinary focus and are prone to data contamination. Comprising around 800 original problems across seven scientific fields, ATLAS aims to enhance the fidelity of evaluations in real-world scientific reasoning.

Read full article

via arXiv — cs.CL

arXiv — cs.CL19 hours ago

ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions

NeutralArtificial Intelligence

ConInstruct is a newly introduced benchmark aimed at evaluating the conflict detection and resolution capabilities of Large Language Models (LLMs). While previous studies have focused on how well LLMs follow user instructions, they often neglect scenarios with conflicting constraints. The benchmark assesses LLMs' performance in detecting and resolving such conflicts, revealing that proprietary models generally perform well, with DeepSeek-R1 and Claude-4.5-Sonnet achieving the highest F1-scores.

Read full article

via arXiv — cs.CL

arXiv — cs.CV19 hours ago

Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomy

PositiveArtificial Intelligence

The integration of Large Language Models (LLMs) with 3D vision is revolutionizing robotic perception and autonomy. This approach enhances robotic sensing technologies, allowing machines to understand and interact with complex environments using natural language and spatial awareness. The review discusses the foundational principles of LLMs and 3D data, examines critical 3D sensing technologies, and highlights advancements in scene understanding, text-to-3D generation, and embodied agents, while addressing the challenges faced in this evolving field.

Read full article

via arXiv — cs.CV

arXiv — cs.CL19 hours ago

Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data

PositiveArtificial Intelligence

Supervised Fine-Tuning (SFT) is essential for adapting Large Language Models (LLMs) to specialized fields like medical reasoning. Current SFT methods often utilize unfiltered datasets, which can be redundant and of low quality, leading to high computational costs and poor performance. This study introduces a new data selection strategy called Difficulty-Influence Quadrant (DIQ), which aims to optimize sample selection based on both difficulty and optimization utility, enhancing the efficiency of medical reasoning applications.

Read full article

via arXiv — cs.CL

arXiv — cs.CV19 hours ago

nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation

PositiveArtificial Intelligence

The nuCarla dataset has been introduced as a large-scale, nuScenes-style bird's-eye view perception dataset designed for the CARLA simulation environment. This dataset addresses the limitations of existing datasets that primarily support open-loop learning by providing a closed-loop simulation framework. nuCarla is fully compatible with the nuScenes format, allowing for the transfer of real-world perception models, and offers a scale comparable to nuScenes, enhancing the training of end-to-end autonomous driving models.

Read full article

via arXiv — cs.CV

arXiv — cs.CL19 hours ago

Harnessing Deep LLM Participation for Robust Entity Linking

PositiveArtificial Intelligence

The article introduces DeepEL, a new framework for Entity Linking (EL) that integrates Large Language Models (LLMs) at every stage of the EL process. This approach aims to enhance natural language understanding by improving entity disambiguation and input representation. Previous methods often applied LLMs in isolation, limiting their effectiveness. DeepEL addresses this by proposing a self-validation mechanism that leverages global context, thus aiming for greater accuracy and robustness in entity linking tasks.

Read full article

via arXiv — cs.CL

arXiv — cs.CL19 hours ago

Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance

PositiveArtificial Intelligence

This study analyzes the transformative role of Large Language Models (LLMs) in research and development (R&D) processes. By automating knowledge discovery, enhancing hypothesis generation, and fostering collaboration within innovation ecosystems, LLMs significantly improve research efficiency and effectiveness. The research highlights how LLMs facilitate more adaptable and informed R&D workflows, ultimately accelerating innovation cycles and reducing time-to-market for groundbreaking ideas.

Read full article

via arXiv — cs.CL

arXiv — cs.CL19 hours ago

Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning

PositiveArtificial Intelligence

The paper titled 'Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning' discusses the potential of Large Language Models (LLMs) in creating agents that can interact with their environment to solve complex problems. It highlights the challenges in applying Reinforcement Learning (RL) to LLMs and the lack of tailored frameworks for training these agents. The authors propose a systematic extension of the Markov Decision Process (MDP) framework to define key components of LLM agents and introduce Agent-R1, a flexible training framework.

Read full article

via arXiv — cs.CL