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.
To alleviate these problems, we propose a multi-vehicle collaborative BEV perception network with adaptive communication loss, called AccBEV, based on conditional variational inference.
Engineering 6.0 · Research 8.0 · Business 5.5
Specifically, the proposed method leverages a one-dimensional array with fixed memory size to facilitate large-scale autonomous flight without boundary constraints.
Engineering 5.5 · Research 7.0 · Business 5.5
To address these limitations, we introduce a simple yet challenging closed-loop evaluation framework that closely integrates real-world driving scenarios into the CARLA simulator with infrastructure cooperation.
Engineering 7.5 · Research 7.0 · Business 5.5
This paper proposes a dynamic adaptive convolutional path planning algorithm for path planning of autonomous vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous driving demands real-time perception that balances accuracy across multiple visual tasks under tight computational budgets.
Engineering 5.5 · Research 7.0 · Business 6.5
We propose AnchDrive, a framework for end-to-end driving that effectively bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, a digital twin (DT)-based cooperative driving system with roadside unit (RSU)-centric architecture is proposed for enhancing safety and efficiency at unsignalized intersections.
Engineering 5.5 · Research 7.0 · Business 6.5
The paper presents the design and implementation of a modular perception and control system for an autonomous surface vehicle (ASV), developed on a System-on-Module (SoM) embedded platform.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this, we introduce UM-Depth, a framework that combines motion- and uncertainty-aware refinement to enhance depth accuracy at dynamic object boundaries and in textureless regions.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: A lexicographic hierarchical planning framework using motion primitives for autonomous vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous driving technology is advancing quickly.
Engineering 5.5 · Research 7.0 · Business 5.5
To address these challenges, we propose MultiLane-CtransFuseNet, a structure-aware framework that integrates lightweight Convolutional Neural Networks (CNNs) with Transformers.
Engineering 5.5 · Research 8.0 · Business 7.0
We introduce CaR1, a novel camera-radar fusion architecture for BEV vehicle segmentation.
Engineering 7.0 · Research 8.0 · Business 5.0
Autonomous-driving research has recently embraced deep Reinforcement Learning (RL) as a promising framework for data-driven decision making, yet a clear picture of how these algorithms are currently employed, benchmarked and evaluated is still missing.
Engineering 7.0 · Research 7.0 · Business 6.5
Our approach utilizes a state-of-the-art image-to-image translation method to generate photorealism-enhanced autonomous driving-related visual data that target the characteristics of the real-world datasets, Cityscapes and KITTI.
Engineering 7.0 · Research 8.0 · Business 5.5
This paper presents KDP, a knowledge-driven diffusion policy that integrates generative diffusion modeling with a sparse mixture-of-experts routing mechanism.
Engineering 5.5 · Research 7.0 · Business 5.0
We developed a model-free control policy combined with FlowDep, an efficient optical flow depth estimation algorithm that computes object depth information using vision.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a modularized architecture and an enhanced design system for miniaturized prototype of a car with extended drive situations which includes traffic sign and light recognition, lane detection, lane change and obstacle detection and avoidance.
Engineering 6.0 · Research 7.0 · Business 5.5
An autonomous driving research paper: Research on multitask framework optimization of A-YOLOM for real-time autonomous driving perception.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we conduct a comprehensive evaluation of existing semantic occupancy prediction models from a reliability perspective for the first time.
Engineering 5.0 · Research 7.0 · Business 5.0