Publications related to Information sharing among autonomous vehicles crossing an intersection

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

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

Identifying aerodynamics of small fixed-wing drones using inertial measurements for model-based navigation

Jan Skaloud, Gabriel François Laupré

The success of drone missions is incumbent on an accurate determination of the drone pose and velocity, which are collectively estimated by fusing iner- tial measurement unit and global navigation satellite system (GNSS) mea- surements. However, during a G ...

2023

A Large-Scale Characterization of How Readers Browse Wikipedia

Tiziano Piccardi, Robert West, Akhil Arora

Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when seeking information. To bridge this gap, we present the first systematic large ...

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

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. The ...

IEEE Institute of Electrical and Electronics Engineers2022

Safety Concerns Emerging from Robots Navigating in Crowded Pedestrian Areas

Aude Billard, Pericle Salvini, Diego Felipe Paez Granados

The slogan “robots will pervade our environment” has become a reality. Drones and ground robots are used for commercial purposes while semi-autonomous driving systems are standard accessories to traditional cars. However, while our eyes have been riveted o ...

2022

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. These encompass visual ...

IEEE2020

Pedestrian Intention Prediction: A Multi-task Perspective

Alexandre Massoud Alahi, Saeed Saadatnejad

In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This w ...

2020

Using A Priori Knowledge to Improve Scene Understanding

Alexandre Massoud Alahi

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but ...

IEEE2019

Small Variance Asymptotics for Non-Parametric Online Robot Learning

Sylvain Calinon, Ajay Kumar Tanwani

Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small va ...

2019

The Navigation of Robotic Fiber Positioners in SDSS-V Project: Design and Implementation

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian

SDSS-V project is one of the major observational cosmological projects which aims to generate the map of the observable universe by collecting spectroscopic data from the sky using optical fibers. Each optical fiber is attached to a robotic positioner to b ...

IEEE2019

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Alexandre Massoud Alahi, Agrim Gupta

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a histor ...

2018

Vehicle Dynamic Model Based Navigation for Small UAVs

Mehran Khaghani

The dominant navigation system for small civilian UAVs today is based on integration of inertial navigation system (INS) and global navigation satellite system (GNSS). This strategy works well to navigate the UAV, as long as proper reception of GNSS signal ...

EPFL2018

Learning nuisances to track pedestrians in autonomous vehicles

Alexandre Massoud Alahi, George Adaimi

Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem a ...

2018

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

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

Assessment of VDM-based autonomous navigation of a UAV under operational conditions

Jan Skaloud, Mehran Khaghani

This paper presents extensions and practical realization of a previously proposed novel approach to navigation and sensor integration for small unmanned aerial vehicles (UAV). The proposed approach employs vehicle dynamic model (VDM) as process model withi ...

Elsevier2018

Vision Based Navigation for Autonomous Cooperative Docking of CubeSats

Camille Sébastien Pirat, Volker Gass

A realistic rendezvous and docking navigation solution applicable to CubeSats is investigated. The scalability analysis of the ESA Autonomous Transfer Vehicle Guidance, Navigation & Control (GNC) performances and the Russian docking system, shows that the ...

2018

Cooperative Perception Algorithms for Networked Intelligent Vehicles

Milos Vasic

The degree of intelligence built-in in today's vehicles in constantly on the rise. The vehicles are being equipped with sensors, with the goal to estimate the state of the vehicle and the environment surrounding it. Intelligent algorithms that process the ...

EPFL2017