AI Automated Driving Assistance system using Lane detection with RNN
We propose a hybrid framework combining a transformer-based curve propagation module with an affinity field-based clustering module.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous Driving Papers with Code, Datasets and Engineering Notes
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.
Risk Map as Middleware: Toward Interpretable Cooperative End-to-End Autonomous Driving for Risk-Aware Planning
To address these limitations, we introduce Risk Map as Middleware (RiskMM) and propose an interpretable cooperative end-to-end driving framework.
Engineering 6.0 · Research 7.0 · Business 5.5
Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and multi-agent collaboration.
Engineering 6.0 · Research 8.5 · Business 6.5
GMF-Drive: Gated Mamba Fusion with Spatial-Aware BEV Representation for End-to-End Autonomous Driving
Second, we propose a novel hierarchical gated mamba fusion (GM-Fusion) architecture that substitutes an expensive transformer with a highly efficient, spatially-aware state-space model (SSM).
Engineering 5.5 · Research 8.0 · Business 5.0
ME3-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced real-time decision-making.
Engineering 5.5 · Research 8.0 · Business 5.0
Vehicle-Mounted Multi-Sensor Fusion Slam System for Autonomous Navigation
The vehicle-mounted multi-sensor fusion simultaneous localization and mapping system (SLAM), aims to solve the problem of accurate positioning and map building for self-driving vehicles in complex environments.
Engineering 5.5 · Research 7.0 · Business 5.0
RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving
In this work, we propose RMT-PPAD, a real-time, transformer-based multi-task model that jointly performs object detection, drivable area segmentation, and lane line segmentation.
Engineering 7.0 · Research 8.0 · Business 5.5
Optimal Trajectory Planning Using Data-Driven Tire-Road Friction Mapping for Autonomous Racing Cars
This paper presents a trajectory planning strategy to optimize lap times by employing minimum curvature optimization, complemented by Gaussian Process models for tire-road friction estimation.
Engineering 5.5 · Research 7.0 · Business 5.5
A Gradient Descent-Based Backend Feedback Adaptive Motion Planning Algorithm for Autonomous Mobile Robots
This article introduces an innovative motion planning algorithm for autonomous mobile robots, specifically focusing on quadrotor unmanned aerial vehicles (UAVs), utilizing a gradient descent-enhanced frontend and backend architecture.
Engineering 5.0 · Research 7.0 · Business 5.0
CARLA Autonomous Driving Using Deep Reinforcement Learning
Artificial intelligence (AI) has made remarkable strides in many technological domains, and one of the most prominent areas of growth in AI is self-driving cars.
Engineering 7.0 · Research 8.0 · Business 5.5
Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
In this work, we introduce Dense Backbone, a lightweight backbone that combines the benefits of high processing speed, lightweight architecture, and robust detection accuracy.
Engineering 6.0 · Research 7.0 · Business 5.5
Efficient MobileNet Backbone and Cascade Fusion in CFRNet for Robust Lane Detection
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
DA-Occ: Direction-Aware 2D Convolution for Efficient and Geometry-Preserving 3D Occupancy Prediction in Autonomous Driving
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
Optimizing Pretrained Autonomous Driving Models Using Deep Reinforcement Learning
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
SE-PointFormer: An Efficient 3D Object Detection Network Based on Image Semantics and Enhanced Point Clouds
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
Vision Transformer-Based Monocular Depth Estimation for Fisheye Cameras
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
Safe Operation Trajectory Planning for Autonomous Excavators
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
DTVAD:End-to-End Autonomous Vehicle Planning Using Reinfocement Learning
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
Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments
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
Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging
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