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.
—Physical adversarial objects-evasion attacks pose a safety concern for automated driving systems (ADS) and are a significant obstacle to their widespread adoption.
Engineering 5.0 · Research 7.0 · Business 5.0
To address the above issues, we propose an environment representation, called Temporal Occupancy Flow Graph (TOFG).
Engineering 5.0 · Research 7.0 · Business 5.0
Finally, we propose a method for synthetic data processing to reduce this domain gap.
Engineering 5.5 · Research 7.0 · Business 5.5
To improve autonomous driving capabilities, we describe a unique deep learning-based fusion architecture in this paper that combines LiDAR and camera data using Birds-Eye-View (BEV) representations.
Engineering 5.0 · Research 7.0 · Business 5.0
Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber, and Tesla investing significantly in self-driving technology.
Engineering 6.5 · Research 7.0 · Business 7.5
This paper presents the development of a system for simulating and visualizing autonomous vehicle navigation using a single-beam LiDAR sensor integrated with an artificial neural network, implemented entirely in JavaScript.
Engineering 5.5 · Research 7.0 · Business 5.5
To tackle the above issues, we propose an adaptive multi-level hypergraph convolution network (AMHGCN), which uses the adaptive multi-level hypergraph representation to capture various dependencies among the human body.
Engineering 5.0 · Research 8.0 · Business 5.0
One important computer vision challenge for increasing the precision and effectiveness of vehicle operations in autonomous driving scenarios is semantic segmentation for self-driving automobiles.
Engineering 5.0 · Research 7.0 · Business 5.0
Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: The Research on Integrated Teaching System of Autonomous Driving Simulation.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, a new depth estimation model named Deep-PDANet based on RC-PDA is proposed, which increases the depth and width of the network and alleviates the problem of network degradation through residual structure.
Engineering 5.5 · Research 7.0 · Business 5.0
Recently, research and development actions have been ongoing to overwhelm the issues with Electric Vehicle (EV) battery systems and long-distance driving limits.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous vehicles (AV) provide many benefits to society.
Engineering 5.5 · Research 7.0 · Business 6.5
We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators.
Engineering 5.0 · Research 7.5 · Business 6.0
We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes.
Engineering 6.5 · Research 8.0 · Business 5.0
We introduce the core concepts and specific methods of various approaches within these categories and summarize their advantages and disadvantages.
Engineering 5.5 · Research 7.0 · Business 5.5
In this paper, we challenge this trend and introduce GenDepth, a novel model capable of performing metric depth estimation for arbitrary vehicle-camera setups.
Engineering 5.5 · Research 8.0 · Business 5.5
An autonomous driving research paper: AR-NET: lane detection model with feature balance concerns for autonomous driving.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: An Autonomous Driving Model Integrated with BEV-V2X Perception, Fusion Prediction of Motion and Occupancy, and Driving Planning, in Complex Traffic Intersections.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we delve deeper into the problem by conducting thorough analyses and demystifying more devils in the details.
Engineering 7.5 · Research 8.0 · Business 5.0