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.
In this paper, we innovatively implement a combination of 2D detectors and raw points within the RoI (region of interest) to filter virtual points to resolve the challenges previously outlined.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles.
Engineering 5.0 · Research 7.0 · Business 5.0
In this study, we introduce a novel multi-modal, density-aware 3D object detection framework, PVNet, which leverages virtual point clouds generated through depth completion to overcome fusion difficulties.
Engineering 5.0 · Research 8.0 · Business 5.0
We present ViTLR, a novel video-based end-to-end neural network that processes multiple consecutive frames to achieve robust traffic light detection and state classification.
Engineering 6.0 · Research 8.0 · Business 6.5
We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images.
Engineering 7.5 · Research 8.0 · Business 6.5
To mitigate these issues, this paper proposes a novel multi-dimension semantic–spatial feature fusion (MDFusion) method that combines LiDAR and image features in 2D and 3D spaces.
Engineering 5.0 · Research 8.0 · Business 5.0
Important tasks in Intelligent Transportation Systems (ITS) for autonomous driving include lane detection, traffic sign detection, and vehicle collision prediction.
Engineering 5.0 · Research 7.0 · Business 5.0
We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving, built upon open-source large language models.
Engineering 7.5 · Research 8.5 · Business 5.0
To mitigate this trade-off, we introduce DuOcc, which employs a dual aggregation strategy that retains dense voxel representations to preserve spatial fidelity while maintaining high efficiency.
Engineering 5.5 · Research 8.0 · Business 5.0
End-to-end autonomous driving learning refers to the process of mapping the original sensor data (such as camera images, radar signals, etc.) directly to driving decisions or control instructions, without the need to manually design complex feature extraction and rule making.
Engineering 5.5 · Research 7.0 · Business 5.0
We introduce GAIA-2, Generative AI for Autonomy, a latent diffusion world model that unifies these capabilities within a single generative framework.
Engineering 5.0 · Research 7.0 · Business 5.0
Based on the depth information, we propose a Sketch-Coloring framework OmniDepth-Occ.
Engineering 5.0 · Research 7.0 · Business 5.0
To address the challenges of lane line recognition failure and insufficient segmentation accuracy in complex autonomous driving scenarios, this paper proposes a dual-branch instance segmentation method that integrates multi-scale modeling and dynamic feature enhancement.
Engineering 5.5 · Research 7.0 · Business 5.0
We propose a robust sensor fusion algorithm that integrates data from a thermal camera, a LiDAR sensor, and a GNSS to provide reliable localization, even in environments where individual sensor data may be compromised.
Engineering 5.5 · Research 7.0 · Business 5.5
In this paper, we introduce Semi-SMD, a novel metric depth estimation framework tailored for surrounding cameras equipment in autonomous driving.
Engineering 7.0 · Research 8.0 · Business 5.0
To tackle this issue, we propose ORION, a hOlistic E2E autonomous dRiving framework by vIsion-language instructed actiON generation.
Engineering 5.5 · Research 8.5 · Business 5.0
This paper proposes a multimodal vehicle trajectory prediction model based on visual perception information (VP-MTP).
Engineering 5.5 · Research 7.0 · Business 6.0
In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems.
Engineering 5.0 · Research 7.5 · Business 5.0
With this work, we propose a framework and scenarios in three different open-source virtual environments, varying in complexity, to test and compare autonomous UAV navigation methods based on vision.
Engineering 6.5 · Research 7.0 · Business 5.0
In this paper, we introduce M3Net, a novel multimodal and multi-task network that simultaneously tackles detection, segmentation, and 3D occupancy prediction for autonomous driving and achieves superior performance than single task model.
Engineering 5.5 · Research 8.0 · Business 5.0