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Concept

Alerte de franchissement involontaire de ligne

Publications associées (26)

The Haptic Link Enabling Driver-Automation Teaming

Jürg Alexander Schiffmann, Robert Fuchs, Tomohiro Nakade

Over-reliance on the automation of transportation systems is understood to be the cause of new types of accidents. In this paper, the automated driving system is augmented with control methods enabling driver intervention without its full deactivation. The ...

IEEE2024

Haptics based collaborative steering control in automated driving environments

Tomohiro Nakade

A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

EPFL2023

Modeling lane changing and lane choice using naturalistic data from a swarm of drones

The pNEUMA dataset is the result of a unique field experiment using a swarm of ten drones flying over the central district of Athens, Greece. Using this newly available dataset, this paper focuses on two directly related topics: i) lane changing and ii) la ...

2022

Reshaping Perception for Autonomous Driving with Semantic Keypoints

Lorenzo Bertoni

The field of artificial intelligence is set to fuel the future of mobility by driving forward the transition from advanced driver-assist systems to fully autonomous vehicles (AV). Yet the current technology, backed by cutting-edge deep learning techniques, ...

EPFL2022

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

2021

Lane Detection and Lane Changing Phenomena Identification with Data from a Swarm of Drones

Nikolaos Geroliminis, Emmanouil Barmpounakis

In the era of big data, new transportation-related concepts and methodologies need to be proposed for understanding how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment with a swarm of drones over a de ...

2020

Lane Detection and Lane-Changing Identification with High-Resolution Data from a Swarm of Drones

Nikolaos Geroliminis, Emmanouil Barmpounakis

In the era of big data, new transportation-related concepts and methodologies need to be proposed to understand how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment using a swarm of drones over a dense ...

National Academy of Sciences2020

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Seungryong Kim

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic perfor ...

IEEE Institute of Electrical and Electronics Engineers2020

Dynamic lane restrictions on congested arterials

Nikolaos Geroliminis

Buses operating in mixed traffic experience problems with delay and travel time variability in peak periods. Numerous transit priority measures are found in the literature but most rely on separating buses from other vehicles. Complete separation such as d ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Huaijian Zhang, Lucian Andrei Gheorghe, Sareh Saeedi, Zahra Khaliliardali, Marija Uscumlic, Ruslan Aydarkhanov

Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain–machine interfaces (BMIs) can provide complementary information that can ease ...

2018

Long-term path prediction in urban scenarios using circular distributions

Alexandre Massoud Alahi

Human ability to foresee the near future plays a key role in everyone's life to prevent potentially dangerous situations. To be able to make predictions is crucial when people have to interact with the surrounding environment. Modeling such capability can ...

2018

Detecting EEG correlates during preparation of complex driving maneuvers

Lucian Andrei Gheorghe

The main focus of this research is the development of real-time recording and analysis devices and algorithms for detecting Electroencephalography (EEG) correlates of steering actions. First, offline analysis where performed in a high fidelity driving simu ...

EPFL2017

Position accuracy with redundant MEMS IMU for road applications

Jan Skaloud, Bertrand Merminod, Pierre-Yves Gilliéron, Philipp Clausen

Traffic problems have mainly resulted from an increase in mobility over the last decades involving an increase in traffic congestion and degradation of environmental conditions. The employment of new systems - namely, Intelligent Transport Systems (ITS) is ...

2015

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles

Alcherio Martinoli, Ali Marjovi, Joseph Chadi Benoit Lemaitre, Milos Vasic

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopa ...

IEEE2015

EEG-based decoding of error-related brain activity in a real-world driving task

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Huaijian Zhang, Lucian Andrei Gheorghe, Zahra Khaliliardali

Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be us ...

Institute of Physics2015

Connectivity Analysis of Brain States and Applications in Brain-Computer Interfaces

Huaijian Zhang

Human brain is organized by a large number of functionally correlated but spatially distributed cortical neurons. Cognitive processes are usually associated with dynamic interactions among multiple brain regions. Therefore, the understanding of brain funct ...

EPFL2015

Network of automated vehicles: The AutoNet2030 vision

Alcherio Martinoli, Ali Marjovi

AutoNet2030 – Co-operative Systems in Support of Networked Automated Driving by 2030 – is a European project connecting two domains of intensive research: cooperative systems for Intelligent Transportation Systems and Automated Driving. Given the latest de ...

2014

Inferring Driver’s Turning Direction through Detection of Error Related Brain Activity

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Huaijian Zhang, Lucian Andrei Gheorghe

This work presents EEG-based Brain-computer interface (BCI) that uses error related brain activity to improve the prediction of driver’s intended turning direction. In experiments while subjects drive in a realistic car simulator, we show a directional cue ...

2013

MUNDUS project: MUltimodal Neuroprosthesis for daily Upper limb Support

Silvestro Micera, Andrea Crema

Background: MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user's direct inv ...

BioMed Central2013

A new collision warning system for lead vehicles in rear-end collisions

Alcherio Martinoli, Sven Adrian Gowal, Adrian Cabrera

Collision Warning Systems (CWS) are safety sys- tems designed to warn the driver about an imminent collision. A CWS monitors the dynamic state of the traffic in real- time by processing information from various proprioceptive and exteroceptive sensors. It ...

Ieee2012