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 this end, we propose Drive-Wm, the first driving world model compatible with existing end-to-end planning models.
Engineering 6.0 · Research 7.0 · Business 5.5
To extend camera-only occupancy estimation into spatiotemporal prediction, we propose Cam4DOcc, a new benchmark for camera-only 4D occupancy forecasting, evaluating the surrounding scene changes in a near future.
Engineering 7.5 · Research 8.0 · Business 6.0
In this paper, we propose Panacea, an innovative approach to generate panoramic and controllable videos in driving scenarios, capable of yielding an unlimited numbers of diverse, annotated samples pivotal for autonomous driving advancements.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes.
Engineering 7.0 · Research 7.0 · Business 5.0
This paper proposes to extend these models with voice commands recognition, spoken by a person in the car, in order to correct the self-driving car movement, to prevent possible traffic accidents, and therefore to increase traffic safety.
Engineering 5.0 · Research 7.0 · Business 5.0
Specifically, it consists of two novel modules: TsAFE and PPFE, enhancing the expression of point cloud features and examining both inter-pillar and intra-pillar relational features, respectively.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we present a systematic investigation in this field.
Engineering 5.0 · Research 7.5 · Business 5.0
With the application of multi-sensor fusion technologies in object detection, Self-driving automobiles have received more and more attention.
Engineering 5.0 · Research 7.0 · Business 5.0
In ESA-funded project VIVAS, we developed a generic framework for system-level simulation-based V&V of autonomous systems.
Engineering 5.0 · Research 7.0 · Business 5.0
Self-driving cars are poised to transform transportation, but ensuring safe and reliable autonomous driving via robust object detection remains a critical challenge.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we experimentally confirm that the patch placement significantly affects the attack success rates, particularly in specific regions.
Engineering 5.0 · Research 8.0 · Business 5.0
Our research aims to generate robust, dense 3-D depth maps for robotics, especially autonomous driving applications.
Engineering 7.0 · Research 8.0 · Business 5.0
To tackle these problems, we propose a novel 3D detection framework called Casgraph, which utilizes dynamic farthest voxel sampling to select representative points within proposed boxes.
Engineering 5.0 · Research 8.0 · Business 5.0
LiDAR-camera fusion is one of the core processes for the perception system of current automated driving systems.
Engineering 5.0 · Research 8.0 · Business 5.0
Lane identification and traffic sign detection is the most challenging and promising problem for self-driving or autonomous vehicles with unintentional lane departure and ignorance of traffic signs being major contributing factors to motor vehicle collisions around the world.
Engineering 5.0 · Research 7.0 · Business 5.5
Our model achieves state-of-the-art (SOTA) results in the vehicle occupancy category on public datasets and achieves SOTA results across all categories on our released HuanYu dataset.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Autonomous Vehicle Driving in Harsh Weather: Adaptive Fusion Alignment Modeling and Analysis.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: A Small-Size 3d Object Detection Network for Analyzing the Sparsity of Raw Lidar Point Cloud.
Engineering 5.0 · Research 7.0 · Business 5.0
More precisely, we develop an attention-based cooperative BEV fusion and prediction model called BEV-V2X.
Engineering 5.0 · Research 8.0 · Business 5.0
The control system architecture development for a full-sized humanoid, HART (Human Assistive RoboT), is presented in this paper.
Engineering 5.0 · Research 7.0 · Business 5.0