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.
The MSU Autonomous Vehicle Simulator (MAVS) is a versatile and customizable software library for simulating autonomous and unmanned ground vehicles in off-road environments.
Engineering 5.0 · Research 7.0 · Business 5.0
Existing trajectory planning methods often face challenges in ensuring stable robot motion control, leading to significant positional errors during navigation.
Engineering 5.5 · Research 8.0 · Business 5.5
In multi-vehicle traffic scenarios, achieving accurate environmental perception and motion trajectory tracking through LiDAR–camera fusion is critical for downstream vehicle planning and control tasks.
Engineering 6.0 · Research 7.0 · Business 6.0
3D occupancy prediction has attracted much attention in the field of autonomous driving due to its powerful geometric perception and object recognition capabilities.
Engineering 6.5 · Research 7.0 · Business 5.0
We propose DiffE2E, a diffusion-based end-to-end autonomous driving framework.
Engineering 7.5 · Research 8.0 · Business 6.0
To address this, we present a generalizable camera simulation framework DriveCamSim, whose core innovation lies in the proposed Explicit Camera Modeling (ECM) mechanism.
Engineering 6.5 · Research 7.0 · Business 5.0
This paper proposes BETAV, a novel framework that addresses the persistent challenges of low 3D perception accuracy and suboptimal trajectory smoothness in autonomous driving systems through unified BEV-Transformer encoding and Bézier-optimized planning.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we present a packetized processing Pillar Feature Net accelerator for LiDAR 3D object detection.
Engineering 5.0 · Research 7.0 · Business 5.0
We introduce FSDrive, a framework that empowers VLAs to"think visually"using a novel visual spatio-temporal CoT.
Engineering 7.5 · Research 8.0 · Business 5.0
To address this, we propose SaliencyAware Quantized Imitation Learning (SQIL), which combines quantization-aware training with a selective lossweighting strategy for mission-critical states.
Engineering 5.5 · Research 7.0 · Business 6.5
In this paper, we are devoted to introducing 3D priors adaptive to lane appearances, which serve as references to aggregate the lane context.
Engineering 5.0 · Research 8.0 · Business 5.0
In this work, we propose a novel uncertainty-aware E2E visual perception and prediction framework that utilized Bird's Eye View (BEV) representations.
Engineering 7.5 · Research 8.0 · Business 5.5
Moreover, the improved predictions significantly benefit downstream planning tasks, highlighting the practical advantages of our method for real-world autonomous driving applications.
Engineering 5.5 · Research 8.0 · Business 5.5
The simulation results demonstrate that our model consistently outperforms SOTA models across nearly all test scenarios.
Engineering 5.5 · Research 8.0 · Business 5.0
Deep learning models, particularly Large Language Models (LLMs), have shown high degrees of complexity in recent years and have, therefore, made effective quantization techniques necessary.
Engineering 6.0 · Research 7.5 · Business 8.0
In this work, we propose a multi-modal Gaussian-based semantic occupancy prediction framework utilizing 3D deformable attention, namely GaussianFormer3D.
Engineering 5.5 · Research 8.0 · Business 5.5
This article presents an alternative variant of motion planning techniques for autonomous vehicles (AVs) centered on an inverse approach that concurrently optimizes both trajectory and speed.
Engineering 5.0 · Research 7.0 · Business 5.0
Here we propose a novel method based on sequential convex optimization, which permits flexible and efficient optimal trajectory generation.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Depth-free view synthesis from diffusion models for monocular 3D detector in autonomous driving.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this issue, this paper presents a successive convex approximation-based fast collision-free motion planning framework to address this issue.
Engineering 5.5 · Research 7.0 · Business 6.0