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.
Our approach employs a lightweight MobileNet-based backbone for real-time feature extraction and a cascade of sub-backbones, each equipped with a triple-level adaptive feature fusion (TAFF) module, to integrate multiscale spatial and contextual cues.
Engineering 5.5 · Research 8.0 · Business 6.0
Efficient and high-accuracy 3D occupancy prediction is vital for the performance of autonomous driving systems.
Engineering 6.0 · Research 7.0 · Business 6.0
In this paper, we present CILRLv3, a DRL-based training method that is immune to CF, enabling pretrained navigation agents to improve their driving skills across new scenarios.
Engineering 5.5 · Research 7.0 · Business 5.0
With the development of autonomous driving technology, the application of 3D object detection in complex dynamic environments has become increasingly important.
Engineering 5.5 · Research 7.0 · Business 5.0
In this paper, we propose the MonoFVT framework, which incorporates a vision transformer (ViT) into the traditional monocular self-supervised depth estimation network, enabling it to better handle the global distortions inherent in fisheye camera images.
Engineering 5.5 · Research 8.0 · Business 6.0
This paper conducts research on trajectory planning in the autonomous operation of hydraulic excavators, aiming to achieve efficient, safe, and energy-saving motion control through algorithmic implementation.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose DTVAD, an RL-based end-to-end autonomous driving framework that directly leverages real-world datasets, specifically the nuScenes dataset, and employs a decision transformer as the planning module.
Engineering 6.5 · Research 7.0 · Business 5.5
An autonomous driving research paper: Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments.
Engineering 5.5 · Research 7.0 · Business 5.0
To overcome these limitations, we developed a self-driving microscope that uses deep learning to predict the onset of aggregation from a single fluorescence image of soluble protein, achieving 91% accuracy.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, we propose PRIX (Plan from Raw pIXels).
Engineering 6.5 · Research 8.0 · Business 6.5
This paper proposes a constrained trajectory optimization framework for autonomous vehicles (AVs) based on convex programming techniques.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these limitations, we propose Depth3DLane, a novel dual-pathway framework that integrates self-supervised monocular depth estimation to provide explicit structural information, without the need for expensive sensors or additional ground-truth depth data.
Engineering 5.0 · Research 8.0 · Business 5.0
This paper aims to develop and evaluate a robust lane detection system for Advanced Driver Assistance Systems (ADAS) using deep learning techniques.
Engineering 5.5 · Research 7.0 · Business 6.5
This study develops an autonomous lane-keeping and steering control system using the CARLA simulator, ROS, and LaneNet for deep learning-based lane detection.
Engineering 5.5 · Research 7.0 · Business 6.0
In this paper, a novel lightweight bird’s-eye view (BEV) architecture is introduced, which fully utilizes the camera data and aims to improve the efficiency of perspective conversion and feature representation.
Engineering 6.0 · Research 8.0 · Business 5.0
Autonomous vehicles completely rely on accurate multi‐sensor fusion to perceive their environment and make driving decisions.
Engineering 5.0 · Research 7.0 · Business 5.0
Specifically, realizing autonomous flight requires a depth sensor to measure distances to obstacles, but many small drones cannot be equipped with one.
Engineering 5.0 · Research 7.0 · Business 5.0
Object recognition is essential for secure and effective navigation via accurate scene interpretation in autonomous vehicle perception systems.
Engineering 5.5 · Research 7.0 · Business 6.0
This paper proposes a deep learning model, named LiDAR Generative Camera (LiGenCam), to fill this gap.
Engineering 5.0 · Research 7.0 · Business 6.0
In this paper, we propose a tightly-coupled multi-sensor filtering framework for robust UAV/UGV state estimation, which integrates data from an Inertial Measurement Unit (IMU), a stereo camera, GPS, and 3D range measurements from two Light Detection and Ranging (LiDAR) sensors.
Engineering 5.0 · Research 7.0 · Business 5.0