Vehicle Detection

Latest papers

8 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Camera Radar LiDAR Fusion via Point-Cloud Projection for Multi-Task Learning
Nov 2, 2026Kavin Chandrasekaran, Sorin GrigorescuVehicle Detection Autonomous Driving Perception
Research and Development Department, Elektrobit Automotive GmbH, Munich, Germany · Department of Robotics, Transilvania University of Brasov, Brasov, Romania
A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring
Sep 22, 2026Tianheng Zhu, Woei-chyi Chang, Alamss Riaz +2Intelligent Transportation Systems Traffic Flow Estimation
Lyles School of Civil and Construction Engineering Purdue University, West Lafayette, IN, USA
NPU Accelerator: Quantized Real-Time Vehicle Detection on PYNQ-Z1 Using FINN
Sep 21, 2026Daniel Gutierrez, Antonio Cuesta, Jorge Fe +2Vehicle Detection FPGA-Based Neural Network Acceleration
Intigia · DFKI
REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
May 12, 2026Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1Camera-Radar Fusion Bird's-Eye-View Representation
Elektrobit Automotive GmbH · Transilvania University of Brasov · Eindhoven University of Technology
BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities
Apr 27, 2026Akash Sharma, Chinmay Mhatre, Sankalp Gawali +8Distribution Shift Object Detection
Department of Computational and Data Sciences (CDS) Indian Institute of Science, Bengaluru, India · Centre for Infrastructure, Sustainable Transportation & Urban Planning (CiSTUP) Indian Institute of Science, Bengaluru, India · Center of Data for Public Good (CDPG) Indian Institute of Science, Bengaluru, India +1
Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
Apr 22, 2026Syed Sajid Ullah, Muhammad Zunair Zamir, Ahsan Ishfaq +1Intelligent Transportation Systems Object Detection
School of Energy and Electrical Engineering, Chang’an University, Xi’an, China · School of Information Engineering, Chang’an University, Xi’an, China
In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
Mar 19, 2026Xiao Fang, Yiming Gong, Stanislav Panev +4Vehicle Detection Adversarial Attacks on Object Detectors
Carnegie Mellon University · DEVCOM Army Research Laboratory · Florida State University
Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
Mar 10, 2026Kanishkha Jaisankar, Pranav M. Pawar, Diana Susan Joseph +4Lane Detection Vehicle Detection
Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE. · Sinhgad Institute of Technology, Lonavala · CTIF Global Capsule, Denmark

Nov 2, 2026J/K move · Enter open · S save

Camera Radar LiDAR Fusion via Point-Cloud Projection for Multi-Task Learning

Kavin Chandrasekaran, Sorin Grigorescu

Research and Development Department, Elektrobit Automotive GmbH, Munich, Germany · Department of Robotics, Transilvania University of Brasov, Brasov, Romania

Vehicle Detection Autonomous Driving Perception Radar-LiDAR Fusion Object Detection Semantic Segmentation

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Autonomous vehicles require robust perception systems to operate safely under diverse environmental conditions. While cameras provide high-resolution visual data, their performance significantly degrades in low-light and adverse weather. High Definition (HD) radar offers superior reliability and angular resolution under such conditions but lacks rich visual feature recognition. LiDAR, though expensive, delivers highly precise distance measurements and dense point clouds. Each sensor exhibits unique trade-offs, necessitating the development of advanced sensor fusion algorithms to harness complementary strengths while minimizing computational complexity and maintaining accuracy. In this study, the proposed fusion architecture performs early fusion by combining camera images with perspective-view point cloud data from HD radar and LiDAR sensors. The proposed model is trained to detect vehicles and segment free drivable space simultaneously. Experiments are performed on the RADIal dataset where we demonstrate that fusing point cloud data with camera images in front-view surpasses the performances of the most recent standalone and fusion models.