XLM for Autonomous Driving Systems: A Comprehensive Review
Large Language Models (LLMs) have showcased remarkable proficiency in various
information-processing tasks. These tasks span from extracting data and summarizing …
information-processing tasks. These tasks span from extracting data and summarizing …
End-to-End Autonomous Driving in CARLA: A Survey
Y Al Ozaibi, MD Hina, A Ramdane-Cherif - IEEE Access, 2024 - ieeexplore.ieee.org
Autonomous Driving (AD) has evolved significantly since its beginnings in the 1980s, with
continuous advancements driven by both industry and academia. Traditional AD systems …
continuous advancements driven by both industry and academia. Traditional AD systems …
AutoReward: Closed-Loop Reward Design with Large Language Models for Autonomous Driving
Autonomous driving technology has made significant strides, with reinforcement learning
(RL) proving crucial due to its superior decision-making capabilities. However, designing …
(RL) proving crucial due to its superior decision-making capabilities. However, designing …
Unified Local-Cloud Decision-Making via Reinforcement Learning
Embodied vision-based real-world systems, such as mobile robots, require a careful
balance between energy consumption, compute latency, and safety constraints to optimize …
balance between energy consumption, compute latency, and safety constraints to optimize …
Neural Volumetric World Models for Autonomous Driving
Effectively navigating a dynamic 3D world requires a comprehensive understanding of the
3D geometry and motion of surrounding objects and layouts. However, existing methods for …
3D geometry and motion of surrounding objects and layouts. However, existing methods for …
SAT: Spatial Aptitude Training for Multimodal Language Models
Spatial perception is a fundamental component of intelligence. While many studies highlight
that large multimodal language models (MLMs) struggle to reason about space, they only …
that large multimodal language models (MLMs) struggle to reason about space, they only …
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Autonomous driving evaluation requires simulation environments that closely replicate
actual road conditions, including real-world sensory data and responsive feedback loops …
actual road conditions, including real-world sensory data and responsive feedback loops …
UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning
K Sengupta, Z Shagguan, S Bharadwaj, S Arora… - arXiv preprint arXiv …, 2024 - arxiv.org
Embodied vision-based real-world systems, such as mobile robots, require a careful
balance between energy consumption, compute latency, and safety constraints to optimize …
balance between energy consumption, compute latency, and safety constraints to optimize …
A Novel MLLM-based Approach for Autonomous Driving in Different Weather Conditions
Autonomous driving (AD) technology promises to revolutionize daily transportation by
making it safer, more efficient, and more comfortable. Their role in reducing traffic accidents …
making it safer, more efficient, and more comfortable. Their role in reducing traffic accidents …