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.
Accurate perception of the surrounding environment is fundamental and essential to safe and reliable autonomous driving.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose an alternative Vision-Geometry-Action (VGA) paradigm that advocates dense 3D geometry as the critical cue for autonomous driving.
Engineering 6.0 · Research 7.0 · Business 5.0
To address this issue, we propose the BEVDrive-E2E to explore the interpretability of the end-to-end model by using visual abstractions in bird's eye view (BEV).
Engineering 5.5 · Research 8.0 · Business 5.0
This survey paper traces the technological history of lane detection systems over the last quarter century, discussing paradigm shifts from classical computer vision methods to modern foundation models.
Engineering 5.0 · Research 7.5 · Business 5.0
To address these challenges, we propose a class-distribution guided active learning framework for selecting training samples to annotate in autonomous driving datasets.
Engineering 5.5 · Research 7.0 · Business 5.0
To address these challenges, this paper presents an instance-centric benchmark for the 3D panoptic occupancy prediction task.
Engineering 5.0 · Research 7.0 · Business 5.0
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations.
Engineering 5.5 · Research 8.5 · Business 5.0
An autonomous driving research paper: Weather-aware Autonomous Driving with Road Condition Analysis using CARLA.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, we propose AutoMoT in this work, an end-to-end AD framework that unifies reasoning and action generation within a single vision-language-action (VLA) model.
Engineering 7.0 · Research 8.0 · Business 5.0
Therefore, we propose a multimodal coarse-to-local transformer (MC2L-Transformer), which is composed of a hierarchical transformer architecture.
Engineering 5.5 · Research 7.0 · Business 5.0
In this work, we design a smart human driving vehicle simulator HDSim which is empowered by cognitively inspired modeling and AI models.
Engineering 5.0 · Research 8.0 · Business 5.0
This paper presents a framework leveraging SParse representation and SCalable feature interaction to address the aforementioned challenges, called SPSC.
Engineering 5.5 · Research 8.0 · Business 5.0
The use of autonomous systems to increase productivity, lessen reliance on manual labor, and reduce operational errors in material handling has increased due to the quick development of industrial automation.
Engineering 5.0 · Research 7.0 · Business 5.0
We introduce a systematic missing-view evaluation protocol on the nuScenes-based SurroundOcc benchmark, encompassing both deterministic single-view failures and stochastic multi-view dropout scenarios.
Engineering 7.5 · Research 7.0 · Business 6.5
Large language models (LLMs) exhibit strong semantic reasoning capabilities for autonomous driving decision-making; however, their substantial inference latency poses a critical challenge for real-time closed-loop vehicle control.
Engineering 5.5 · Research 7.5 · Business 5.0
The rapid evolution of autonomous driving has made high-fidelity simulation essential for validating safety-critical algorithms.
Engineering 7.5 · Research 7.0 · Business 5.5
Autonomous robotic systems are increasingly deployed across manufacturing, logistics, and healthcare due to their ability to efficiently complete difficult and dangerous tasks.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this and advance sustainable autonomous driving, this paper proposes a Bird’s-Eye View (BEV)-based multi-modal 3D object detection approach tailored for nighttime scenarios, integrating low-light adaptive components while preserving the original BEV pipeline.
Engineering 6.0 · Research 8.0 · Business 6.0
To address these challenges, we propose Dr.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this, we introduce a novel task: unobserved road segmentation, focusing on explicitly identifying road regions occluded from the ego-vehicle’s view.
Engineering 5.0 · Research 8.0 · Business 5.0