Cooperative Perception for Networked Intelligent Vehicles

The goal of this collaborative research is to improve driver safety and overall traffic fluidity using innovative intelligent vehicle technology.

This project focuses on the development of novel cooperative perception algorithms for intelligent vehicles operating in realistic traffic scenarios. Scientific challenges are concerned with designing an efficient sensory system, as well as designing perception and sensor fusion algorithms that involve V2X communication. In terms of algorithmic validation, we will work both with real vehicles, provided by PSA Peugeot-Citroën, and simulation tools at various abstraction levels.

This project was featured in the EPFL news.

Team and Collaborators

Sponsors and Research Period

This project was sponsored by PSA (Peugeot Citroën Automobiles SA), from September 2012 to August 2017.

Related Student Projects and Internships

DISAL-MP33: Johannes Brakker Løje, Gaussian Process Labeled Multi-Bernoulli Filter for Tracking in Dynamic Environments

DISAL-MP32: Romain Michael Desarzens, Multiple Extended Target Tracking Based on PHD Filter and Gaussian Processes

DISAL-SP85: Jonathan Gan, A Collaborative Fusion and Tracking Algorithm Based on a Sequential Monte Carlo Probability Hypothesis Density Filter

DISAL-SP79: Raphaël Lüthi, Object Classification in Urban Environments by Means of Machine Learning Techniques

DISAL-SP88: David Rivollet, Cooperative Localization Based on Topology Matching

DISAL-SP89: Loïc Veyssière, Comparison of Centralized and Distributed Fusion Architectures in a Sensor Network

DISAL-SP66: Gael Lederrey, Collaborative Sensing and Decision Making for Intelligent Vehicle Maneuvers

DISAL-SP55: Titus Cieslewski, Feature-Based Localization for Autonomous Vehicles

DISAL-SP59: Luca Brusatin, Detection and Classification Using Data from an Automotive Laser Range-Finder




Cooperative Perception Algorithms for Networked Intelligent Vehicles

M. Vasic / A. Martinoli (Dir.)  

Lausanne, EPFL, 2017. 

Challenges for Automated Cooperative Driving: The AutoNet2030 Approach

M. Obst; A. Marjovi; M. Vasic; I. Navarro Oiza; A. Martinoli et al. 

Automated Driving; Switzerland: Springer International Publishing, 2017. p. 561-570.


Cooperative Multiple Dynamic Object Tracking on Moving Vehicles Based on Sequential Monte Carlo Probability Hypothesis Density Filter

J. Gan; M. Vasic; A. Martinoli 

2016. IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, November 2016. p. 2163-2170. DOI : 10.1109/ITSC.2016.7795906.

A System Implementation and Evaluation of a Cooperative Fusion and Tracking Algorithm based on a Gaussian Mixture PHD Filter

M. Vasic; D. Mansolino; A. Martinoli 

2016. IEEE/RSJ International Conference on Intelligent Robots and Systems, Korea, October, 2016. p. 4172-4179. DOI : 10.1109/IROS.2016.7759614.

An Overtaking Decision Algorithm for Networked Intelligent Vehicles Based on Cooperative Perception

M. Vasic; G. Lederrey; I. Navarro; A. Martinoli 

2016. IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016. p. 1054-1059. DOI : 10.1109/IVS.2016.7535519.


A Collaborative Sensor Fusion Algorithm for Multi-Object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

M. Vasic; A. Martinoli 

2015. IEEE International Conference on Intelligent Transportation Systems, Las Palmas de Gran Canaria, Spain, September 2015. p. 491-498. DOI : 10.1109/ITSC.2015.87.