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 present a new vertical slice representation that divides the scene along the vertical axis and projects spatial point features onto the nearest pair of parallel planes.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles Using V2X Communication and Hybrid Computing.
Engineering 5.0 · Research 7.0 · Business 5.0
We propose a novel transformer-based Diffusion Planner for closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement.
Engineering 6.0 · Research 8.0 · Business 5.5
To address the limitations of directly applying LiDAR-oriented encoders to sparse radar data, we propose a Radar Height Self-Attention module that enhances vertical spatial reasoning and feature extraction.
Engineering 7.5 · Research 8.0 · Business 6.5
In this paper, we propose Doracamom, the first framework that fuses multi-view cameras and 4D radar for joint 3D object detection and semantic occupancy prediction, enabling comprehensive environmental perception.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Metamorphic testing of deep neural network-based autonomous driving systems using behavioural domain adequacy.
Engineering 5.0 · Research 7.0 · Business 5.0
Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling, fundamentally transforming how vehicles interpret dynamic scenes and execute safe decision-making.
Engineering 5.5 · Research 7.0 · Business 6.0
This paper proposes a method for integrating the electronic power steering (EPS) system into autonomous driving control by interfacing the CARLA simulator with the Robot Operating System (ROS).
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Research on Automatic Driving System Based on End-to-End and Real-Time Path Planning.
Engineering 5.5 · Research 7.0 · Business 5.0
Autonomous vehicles depends on various sensors to accurately perceive their surroundings, ensuring safe and efficient navigation.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we propose STViT+, a novel Transformer-based framework for self-supervised multi-camera depth estimation.
Engineering 5.5 · Research 8.0 · Business 5.0
Research on integrating camera and LiDAR in self-driving car systems has important scientific significance in the context of developing 4.0 technology and applying artificial intelligence.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous driving vehicles have strong path planning and obstacle avoidance capabilities, which provide great support to avoid traffic accidents.
Engineering 5.5 · Research 7.0 · Business 5.0
Advancements in lane detection, leveraging semantic knowledge, have enhanced the detection capabilities of intelligent vehicles in various traffic scenarios.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we undertake a comprehensive analysis of the impact of snowfall conditions on LiDAR imaging and the nature of interference to the object.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we investigate the impact of various kinds of occlusions on camera sensor by projecting their effects from multi-view camera images of the nuScenes dataset into the Bird's-Eye View (BEV) domain.
Engineering 5.5 · Research 7.0 · Business 5.0
We present a novel framework called UPAQ, which leverages semi-structured pattern pruning and quantization to improve the efficiency of LiDAR point-cloud and camera-based 3D object detectors on resource-constrained embedded AV platforms.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this, we introduce DriveBench, a benchmark eval-uating 12 VLMs across 17 settings, covering 19,200 images, 20,498 QA pairs, and four key driving tasks.
Engineering 5.0 · Research 7.0 · Business 5.0
Self-driving cars require extensive testing, which can be costly in terms of time.
Engineering 5.0 · Research 8.0 · Business 5.0