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 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
Therefore, we propose a new multimodal detection strategy, Semantic-Enhanced and Temporally Refined Bidirectional BEV Fusion (SETR-Fusion).
Engineering 5.5 · Research 8.0 · Business 5.0
To address this problem, this paper proposes a fast and deployable 3D object detection method from the LiDAR point cloud for autonomous driving, named Fade3D.
Engineering 7.5 · Research 7.0 · Business 7.0
We propose MTC-BEV, a novel multi-modal 3D object detection framework for autonomous driving that achieves robust and efficient perception by combining spatial, temporal, and semantic cues.
Engineering 5.5 · Research 8.0 · Business 5.0
Autonomous surface vehicles (ASVs) are widely employed in various marine industrial tasks.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Predictive Display for Teleoperation Based on Vector Fields Using Lidar-Camera Fusion.
Engineering 5.0 · Research 7.0 · Business 5.0
To solve the problems, this paper proposes a two-stage monocular metric depth estimation with boundary calibration and geometric optimization.
Engineering 5.0 · Research 8.0 · Business 5.0
Methods Therefore, we propose a novel End-to-End model for autonomous driving on temporary roads specifically designed for mobile robots.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we introduce a weakly supervised learning strategy that incorporates semantic segmentation, instance segmentation, and optical flow as additional sources of supervision.
Engineering 5.0 · Research 8.0 · Business 5.0
Focusing on the development of an end-to-end autonomous vehicle model with pixel-to-pixel context awareness, this research proposes the SKGE-Swin architecture.
Engineering 6.0 · Research 7.0 · Business 5.5
The development of autonomous vehicle technology has become a central focus in the global transformation industry.
Engineering 5.5 · Research 7.0 · Business 6.0
Object detection is one of the main applications of computer vision based on artificial intelligence (AI), with broad benefits in various fields such as safety monitoring, autonomous vehicles, smart agriculture, and underwater environmental observation.
Engineering 5.0 · Research 7.0 · Business 5.0