Deep Learning Based Lane Detection for Auto Driving Vehicles
This paper presents a robust lane detection system based on deep learning.
Engineering 5.5 · Research 7.0 · Business 6.0
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.
Autonomous Driving Behavior Decision-Making and Control Command Generation Based on Domain Generalization
We propose a method based on domain invariant feature extraction, which removes factors unrelated to driving such as weather and lighting in traffic scenarios, while retaining domain invariant features closely related to driving such as pedestrians, vehicles, and roads.
Engineering 5.5 · Research 7.0 · Business 5.0
A Review of Deep Learning-Based Lane Detection Methods in Complex Environments
Lane detection is pivotal for enhancing the safety and functionality of Advanced Driver Assistance Systems (ADAS) and autonomous driving.
Engineering 5.5 · Research 7.0 · Business 6.5
The Lane Detection Model Based on Data Augmentation and Deep Learning
Lane detection serves as a cornerstone task in autonomous driving systems, as it directly impacts the vehicles ability to maintain lane discipline, ensure safety, and perform accurate path planning.
Engineering 5.5 · Research 7.0 · Business 6.5
Multiscale Video Transformers for Class Agnostic Segmentation in Autonomous Driving
We develop multiscale video transformers capable of detecting unknown objects using only motion cues.
Engineering 5.5 · Research 8.5 · Business 5.0
SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
We present SlimComm, a communication-efficient framework that integrates 4D radar Doppler with a query-driven sparse scheme.
Engineering 5.0 · Research 7.0 · Business 5.0
Self-Driving Car: A Deep Learning Approach
-- A self-driving car, an automated car or an autonomous vehicle, is a robotic vehicle that is designed to travel between destinations without a human operator.
Engineering 5.0 · Research 7.0 · Business 5.0
A novel framework for trajectory planning in robotic arm developed by integrating dynamical movement primitives with particle swarm optimization
To address these challenges, a novel framework (DMP-PSO) for trajectories planning in robotic arm is presented by integrating dynamical movement primitives (DMP) with particle swarm optimization (PSO) in this paper.
Engineering 5.0 · Research 8.0 · Business 5.0
Hybrid Lane Detection and Turn Prediction Framework using U-Net-based Lane Marking Visibility and Geometric Curve Analysis
Autonomous driving systems rely heavily on accurate lane detection and turn prediction for safe and reliable navigation.
Engineering 5.5 · Research 7.0 · Business 5.5
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
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