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.
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
This paper presents a robust lane detection system based on deep learning.
Engineering 5.5 · Research 7.0 · Business 6.0
We propose a method based on domain invariant feature extraction, which removes factors unrelated to driving such as weather and lighting in traffic scenarios, while retaining domain invariant features closely related to driving such as pedestrians, vehicles, and roads.
Engineering 5.5 · Research 7.0 · Business 5.0
Lane detection is pivotal for enhancing the safety and functionality of Advanced Driver Assistance Systems (ADAS) and autonomous driving.
Engineering 5.5 · Research 7.0 · Business 6.5
Lane detection serves as a cornerstone task in autonomous driving systems, as it directly impacts the vehicles ability to maintain lane discipline, ensure safety, and perform accurate path planning.
Engineering 5.5 · Research 7.0 · Business 6.5
We develop multiscale video transformers capable of detecting unknown objects using only motion cues.
Engineering 5.5 · Research 8.5 · Business 5.0
We present SlimComm, a communication-efficient framework that integrates 4D radar Doppler with a query-driven sparse scheme.
Engineering 5.0 · Research 7.0 · Business 5.0
-- A self-driving car, an automated car or an autonomous vehicle, is a robotic vehicle that is designed to travel between destinations without a human operator.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, a novel framework (DMP-PSO) for trajectories planning in robotic arm is presented by integrating dynamical movement primitives (DMP) with particle swarm optimization (PSO) in this paper.
Engineering 5.0 · Research 8.0 · Business 5.0
Autonomous driving systems rely heavily on accurate lane detection and turn prediction for safe and reliable navigation.
Engineering 5.5 · Research 7.0 · Business 5.5