Camera-radar 3D perception model for autonomous driving H/F

CEA; Saclay; Sur place; EUR 40 000 - 60 000; Temps partiel

Résumé du poste

A leading energy and atomic research organization in Île-de-France is offering an internship focusing on advanced methods for fusing information from cameras and radars for improved 3D understanding in driving environments. The role involves designing deep learning models for multi-sensor fusion and contributing to research reports. Ideal for students in related fields, this opportunity allows hands-on experience in cutting-edge research.

Responsabilités

• Review the state of the art on camera-radar fusion for 3D perception.
• Design, develop and evaluate a novel deep learning model for multi-sensor fusion.
• Contribute to research reports and potential publications.

Description du poste

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas:

• defence and security
• nuclear energy (fission and fusion)
• technological research for industry
• fundamental research in the physical sciences and life sciences

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centres spread throughout France.

Reference

2025-37669

Category

Mathematics, information, scientific, software

Contract

Internship

Job title

Subject 6

Internship Objective

The goal of this internship is to investigate advanced methods for fusing complementary information from cameras and radars to achieve a robust and accurate 3D understanding of the driving environment (3D detection, 3D semantic segmentation...). Cameras provide rich semantic and structural cues for object recognition and scene interpretation, while radars deliver precise range and velocity measurements that remain reliable under adverse weather and lighting conditions. By combining these modalities, the intern will explore novel deep learning architectures for multi-sensor fusion, with the aim of improving accuracy in complex driving scenarios.

Responsibilities

• Review the state of the art on camera-radar fusion for 3D perception
• Design, develop and evaluate a novel deep learning model for multi-sensor fusion
• Contribute to research reports and potential publications