Browse curated autonomous driving papers on end-to-end driving, BEV perception, 3D object detection, motion prediction, path planning, ADAS, Tesla FSD, Waymo, and self-driving foundation models.
In this paper, we present VLP, a novel Vision-Language-Planningframework that exploits language models to bridge the gap between linguistic understanding and autonomous driving.
Engineering 6.0 · Research 8.0 · Business 5.0
In this paper, an optimal design method based on BPNN (BP neural network) is adopted to design the intelligent control system of SDA trajectory.
Engineering 5.0 · Research 7.0 · Business 5.0
Specifically, in an experiment, our investigation revealed a notable discrepancy in the LiDAR reflection intensity within a point cloud scene, with stronger intensities observed in proximity and weaker intensities observed at a distance.
Engineering 5.0 · Research 8.0 · Business 5.0
For autonomous driving, various sensors such as cameras, LiDAR, and radar are required to accurately perceive the surrounding environment.
Engineering 5.0 · Research 7.0 · Business 5.0
For the commercialization of autonomous vehicles, precise perception based on three-dimensional (3D) spatial recognition is imperative.
Engineering 5.0 · Research 7.0 · Business 6.0
Autonomous driving has attracted significant attention from both academia and industries, and it is expected to offer a safer and more efficient driving system.
Engineering 5.0 · Research 7.0 · Business 5.0
To bridge these gaps, we introduce NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17 subtasks, where each task demands holistic information (e.g., temporal, multi-view, and spatial), significantly elevating the challenge level.
Engineering 7.0 · Research 8.5 · Business 5.0
To this end, we present an exhaustive study of 265 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions.
Engineering 5.0 · Research 8.0 · Business 5.0
Developing a self-driving system is a challenging task that requires a high level of scene comprehension with real-time inference, and it is safety-critical.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Barrier-Enhanced Homotopic Parallel Trajectory Optimization for Safety-Critical Autonomous Driving.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a safe and robust motion planning system tailored for autonomous navigation of quadruped robots in cluttered environments.
Engineering 5.5 · Research 7.0 · Business 5.5
Planning safe and smooth trajectories for multiple autonomous ground vehicles (MAGVs) in a complex dynamic unstructured environment is a fundamental and challenging task.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this, we introduce a multi-modal instruction tuning dataset that facilitates language models in learning visual instructions across diverse driving scenarios.
Engineering 6.5 · Research 7.0 · Business 5.0
In response, this paper presents DriveLLM, a decision-making framework that integrates large language models (LLMs) with existing autonomous driving stacks.
Engineering 5.5 · Research 8.5 · Business 5.5
The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving.
Engineering 5.5 · Research 7.0 · Business 5.5
An autonomous driving research paper: Path planning algorithms in the autonomous driving system: A comprehensive review.
Engineering 5.0 · Research 7.0 · Business 5.0
—Physical adversarial objects-evasion attacks pose a safety concern for automated driving systems (ADS) and are a significant obstacle to their widespread adoption.
Engineering 5.0 · Research 7.0 · Business 5.0