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.
An autonomous driving research paper: Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking.
Engineering 5.0 · Research 7.0 · Business 5.0
In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in the following: 1) an unsupervised intersection crossing and 2) the platooning scenarios.
Engineering 5.0 · Research 8.0 · Business 5.0
To alleviate this problem, we proposed a lightweight transformer-based end-to-end model with risk awareness ability for AV decision-making.
Engineering 6.0 · Research 7.0 · Business 6.0
In this paper, we implemented a deep learning-based motion planner using sensor fusion from LiDAR point clouds and camera RGB images to predict future waypoints.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes.
Engineering 7.0 · Research 7.0 · Business 5.0
Driver behaviour is a significant factor in the smooth driving of vehicles on the roads.
Engineering 5.5 · Research 7.0 · Business 6.0
Object detection in point clouds is a critical component in most autonomous driving systems.
Engineering 5.0 · Research 8.0 · Business 5.0
To tackle this problem, we propose an event-vision based framework that integrates low-light enhancement for the RGB source, and exploits the complementary merits of RGB and event data.
Engineering 5.0 · Research 7.0 · Business 5.0
To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes.
Engineering 5.5 · Research 8.0 · Business 5.0
In this article, we propose a cross-view transformer-based network, dubbed CVTNet, to fuse the range image views and bird's eye views generated from the LiDAR data.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: Fully convolutional neural networks for LIDAR–camera fusion for pedestrian detection in autonomous vehicle.
Engineering 5.0 · Research 7.0 · Business 5.0
To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
Engineering 7.5 · Research 8.0 · Business 5.5
Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
Engineering 5.0 · Research 8.0 · Business 5.0
This paper presents a 3D point cloud stitching method for object detection with wide horizontal field of view (FoV) using roadside LiDAR.
Engineering 5.0 · Research 7.0 · Business 5.0
Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread.
Engineering 7.0 · Research 7.0 · Business 6.0
This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing faster BEV perception on the on-vehicle chips.
Engineering 6.0 · Research 7.0 · Business 6.0
This paper proposes a method of achieving autonomous navigation using light weight embedded system and affordable monocular cameras by combining features of image processing and resource sharing.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing real-time BEV perception on the on-vehicle chips.
Engineering 7.5 · Research 8.0 · Business 7.0
In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection.
Engineering 5.0 · Research 7.0 · Business 5.0
Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks.
Engineering 5.5 · Research 8.0 · Business 5.5