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.
Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems.
Engineering 5.5 · Research 8.0 · Business 5.0
In this work, we propose a new multi-modal visual grounding task, termed LiDAR Grounding.
Engineering 5.0 · Research 7.0 · Business 5.0
We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues.
Engineering 7.0 · Research 8.0 · Business 5.0
This paper proposes a motion planning and tracking control method based on model predictive control (MPC) for autonomous underwater vehicles (AUVs).
Engineering 5.0 · Research 7.0 · Business 5.0
End-to-end autonomous driving has made impressive progress in recent years.
Engineering 5.5 · Research 8.0 · Business 5.0
Nowadays, autonomous driving has recently started to attract more attention; therefore, numerous automotive companies are developing various techniques for semi-autonomous to fully-autonomous driving systems.
Engineering 5.0 · Research 8.0 · Business 5.0
To address these challenges, we propose a two-stage integrated neural planning framework, termed OPGP, that incorporates joint prediction guidance from occupancy forecasting.
Engineering 5.5 · Research 8.0 · Business 5.5
In this paper, we investigate the possibility of physically fooling LiDAR-based 3D object detection by injecting adversarial point clouds using lasers.
Engineering 5.0 · Research 7.0 · Business 6.0
To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene.
Engineering 7.0 · Research 7.0 · Business 6.0
Many exciting future research topics in the field of Cooperative Autonomous Vehicles (CAVs) require the simulation of both connectivity and automation components.
Engineering 5.0 · Research 7.0 · Business 5.0
In particular, we introduce a Full-Scale depth prediction network named FSNet.
Engineering 6.0 · Research 7.0 · Business 6.0
This paper proposes an improved bidirectional feature pyramid module (BiFPN) and a channel attention module (Seblock: squeeze and excitation) to address these issues in existing methods based on monocular camera sensor.
Engineering 5.5 · Research 8.0 · Business 5.0
This paper presents OccFormer, a dual-path transformer network to effectively process the 3D volume for semantic occupancy prediction.
Engineering 7.0 · Research 8.0 · Business 5.0
In this paper, we propose a novel guided attention architecture, EGA-Depth, which can improve both the efficiency and accuracy of self-supervised multi-camera depth estimation.
Engineering 5.5 · Research 8.0 · Business 5.0
Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems.
Engineering 5.0 · Research 7.0 · Business 5.0
Self-driving cars have become a trending subject with a significant improvement in the technologies in the last decade.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, we present NVAutoNet: a specialized Bird’s-Eye-View (BEV) perception network tailored explicitly for automated vehicles.
Engineering 5.5 · Research 7.0 · Business 6.0
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Engineering 8.0 · Research 8.0 · Business 6.5
Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.
Engineering 7.5 · Research 7.0 · Business 5.5
On this basis, we propose PMPF, point-cloud multiple-pixel fusion, for 3D object detection.
Engineering 5.0 · Research 8.0 · Business 5.0