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Concept

Vehicular automation

Publications associées (28)

Less Conservatism, Stronger Robustness: Iterative Robust Gain-Scheduled Path Following Control of Autonomous Bus With Unstructured and Changing Dynamics

Denis Gillet

Path-following control is a critical technology for autonomous vehicles. However, time-varying parameters, parametric uncertainties, external disturbances, and complicated environments significantly challenge autonomous driving. We propose an iterative rob ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Alexandre Massoud Alahi

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

IEEE Institute of Electrical and Electronics Engineers2024

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

Haptics based multi-level collaborative steering control for automated driving

Hannes Bleuler, Jürg Alexander Schiffmann, Robert Fuchs, Tomohiro Nakade

Increasing the capability of automated driving vehicles is motivated by environmental, productivity, and traffic safety benefits. But over-reliance on the automation system is known to cause accidents. The role of the driver cannot be underestimated as it ...

2023

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

Mathieu Salzmann, Jiancheng Yang

3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle t ...

Ieee Computer Soc2023

Chaotic microcomb inertia-free parallel ranging

Tobias Kippenberg, Johann Emmeram Riemensberger, Anton Lukashchuk, Anton Stroganov

Ever growing pixel acquisition rates in the fields of augmented reality, autonomous driving and robotics have increased interest in solid state beam scanning without moving parts. Modern photonic integrated laser ranging advances towards passive beam steer ...

AIP Publishing2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

Reliable strategies for implementing model-based navigation on fixed-wing drones

Jan Skaloud, Gabriel François Laupré

A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of mod ...

2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Tobias Kober, Davide Piccini

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Wiley2023

Implicit Distance Functions: Learning and Applications in Robotics

Mikhail Koptev

In this thesis, we address the complex issue of collision avoidance in the joint space of robots. Avoiding collisions with both the robot's own body parts and obstacles in the environment is a critical constraint in motion planning and is crucial for ensur ...

EPFL2023

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

EPFL2023

Improving hand prosthesis control with advanced single-finger proportional decoding and robotic shared control

Vincent Alexandre Mendez

Myoelectric prostheses allow users to recover lost functionality by controlling a robotic device with their remaining muscle activity. Commercial devices usually use a two-recording-channel system placed on specific muscles to control a single degree of fr ...

EPFL2023

Robust Training and Verification of Deep Neural Networks

Fabian Ricardo Latorre Gomez

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

EPFL2023

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

Rachid Guerraoui, Alexandre David Olivier Maurer

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Assoc Computing Machinery2023

Pedestrian 3D Bounding Box Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yi Zhou Ju

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

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

Long Term Motion Prediction Using Keyposes

Pascal Fua, Mathieu Salzmann, Wei Wang, Sena Kiciroglu

Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. ...

IEEE2022

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Isinsu Katircioglu

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

EPFL2022