OmniNAV: Omniscient Navigation via Unified LiDAR–Camera BEV Fusion for End-to-End Autonomous Driving
Accepted at IEEE IV 2026
Engineering 5.5 · Research 7.0 · Business 5.0
Sensor Fusion - Autonomous Driving Papers, Code and Research Roadmap
Multi-sensor fusion for autonomous driving — combining LiDAR, camera, radar and ultrasonic sensors for robust all-weather environment perception.
SceneMiner: Identity-Preserving Multi-Task Fine-Tuning for Unified BEV Scene Mining
Mining hard, safety-critical scenes from driving logs is bottlenecked by the absence of difficulty labels, and no single proxy, collision risk, trajectory ambiguity, or semantic rarity suffices to find such scenes on its own.
Engineering 6.5 · Research 7.0 · Business 5.0
CAMF-Det: Closure-Aware Multimodal Fusion for LiDAR-Camera 3D Object Detection on UAV Platforms
Multimodal 3D object detection based on LiDAR and cameras has demonstrated excellent performance in ground-vehicle scenarios, but has not been explored for Unmanned Aerial Vehicle (UAV) platforms.
Engineering 5.0 · Research 7.0 · Business 5.0
Research on Calibration and Hardware Acceleration of Multi-Sensor Fusion Perception System in Autonomous Driving
An autonomous driving research paper: Research on Calibration and Hardware Acceleration of Multi-Sensor Fusion Perception System in Autonomous Driving.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous Navigation System for Library Service Robot Based on Unitree Go2 Edu
This paper presents a ROS 2 navigation system for a Unitree Go2 Edu quadruped equipped with a 4D LiDAR, a front depth camera, and an IMU.
Engineering 5.5 · Research 7.0 · Business 6.0
Robust target detection method against weather interference based on multisource sensor fusion
To address this issue, we propose SeparateFusion, a novel multisensor fusion framework that integrates four-dimensional (4D) millimeter-wave radar and LiDAR data via a deep neural network.
Engineering 5.0 · Research 8.0 · Business 5.0
Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
This paper presents a comprehensive survey of 138 works, primarily published between 2015 and 2025, spanning both classical and learning-based approaches.
Engineering 5.0 · Research 7.0 · Business 5.0
4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather
Our approach extends two representative backbones: a radar-camera pipeline where radar substitutes LiDAR, and a LiDAR-radar pipeline where radar complements LiDAR.
Engineering 5.0 · Research 7.0 · Business 5.0
Semantics-Guided Multimodal Masked Autoencoder Pretraining for 3D BEV Object Detection
Accurate 3D bird's-eye view (BEV) object detection is essential for autonomous driving, and depends strongly on effective multimodal representations from complementary sensors such as cameras and LiDAR.
Engineering 5.5 · Research 7.0 · Business 5.0
HOPNet: Heterogeneous Object Priority Network for Unusual Object Detection in Mixed Traffic
This paper proposes HOPNet (Heterogeneous Object Priority Network), a multi-modal real-time object detection framework integrating YOLOv8-based convolutional neural networks with Swin Transformer attention, camera-LiDAR fusion, and domain-adaptive transfer learning.
Engineering 5.0 · Research 8.0 · Business 5.0
Design and Implementation of Multi-Sensor Perception System for Autonomous Vehicle
This paper provides an in-depth exploration of a design and implementation of multi-sensor perception system for autonomous vehicle (AV).
Engineering 5.5 · Research 7.0 · Business 6.0
MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation
We present MORPH-U, a CARLA-based closed-loop stack that fuses LiDAR/radar/camera with V2X (CAM/DENM) into a Local Dynamic Map (LDM) and triggers Hybrid-A* replanning when validated hazards or map changes affect the planned route.
Engineering 5.0 · Research 7.0 · Business 5.0
Enhancing BEV Perception Through Vehicle-Road Cooperative Systems: An Attention-Based Cross-View Fusion Approach
This paper presents a novel vehicle-to-infrastructure (V2I) cooperative perception framework to address inherent limitations of bird's eye view (BEV) systems in autonomous driving.
Engineering 5.0 · Research 8.0 · Business 5.0
SPSC: Sparse and Scalable Multi-Modal 3D Occupancy Prediction for Autonomous Driving
This paper presents a framework leveraging SParse representation and SCalable feature interaction to address the aforementioned challenges, called SPSC.
Engineering 5.5 · Research 8.0 · Business 5.0
AGNI: Adaptive Sensor Fusion and Terrain-Aware Navigation for Autonomous Ground Vehicles
This paper presents the mechanical design, sensor fusion strategy, path planning algorithms, and experimental validation of AGNI in challenging terrains.
Engineering 5.0 · Research 7.0 · Business 5.0
Automatic Multi-Sensor Calibration for Autonomous Vehicles: A Rapid Approach to LiDAR and Camera Data Fusion
Precise sensor integration is crucial for autonomous vehicle (AV) navigation, yet traditional extrinsic calibration remains costly and labor-intensive.
Engineering 5.5 · Research 7.0 · Business 5.5
Recent Development in Radar AI Sensor Fusion for Autonomous Vehicle
In this paper, we give a brief look at the sensors and how they work together in autonomous vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
Deep Sensor Fusion Framework for Autonomous Vehicle Perception
Accurate perception in autonomous vehicles is critical for ensuring safety and reliability in complex driving environments.
Engineering 5.5 · Research 7.0 · Business 6.0
AutoNav: A CARLA-Based Framework for Autonomous Vehicle Simulation
The paper explores the way that the design of autonomous vehicle simulator creates a virtual environment inside which the high-cost and safety-innovative, autonomous vehicle (AV) technologies could be achieved through innovation and experimentation.
Engineering 7.0 · Research 7.0 · Business 5.5
Real Time Steering Angle Estimation Using Monocular Depth Profiling and 1D Free Space Analysis
We propose a novel, cost effective approach that uses a monocular camera and deep learning-based depth estimation to generate steering commands in real time.
Engineering 5.0 · Research 8.0 · Business 5.0