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20202023
most citedLearning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

8 citations · 9 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV2023

Analysis over vision-based models for pedestrian action anticipation

Lina Achaji, Julien Moreau, François Aioun +1

Anticipating human actions in front of autonomous vehicles is a challenging task. Several papers have recently proposed model architectures to address this problem by combining mul…

cs.CV2022

PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer

Lina Achaji, Thierno Barry, Thibault Fouqueray +3

Nowadays, our mobility systems are evolving into the era of intelligent vehicles that aim to improve road safety. Due to their vulnerability, pedestrians are the users who will ben…

cs.CV2021

Is attention to bounding boxes all you need for pedestrian action prediction?

Lina Achaji, Julien Moreau, Thibault Fouqueray +2

The human driver is no longer the only one concerned with the complexity of the driving scenarios. Autonomous vehicles (AV) are similarly becoming involved in the process. Nowadays…

cs.CV2020★ 8 cited

Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

Donghao Xu, Zhezhang Ding, Xu He +4

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning mod…

cs.CV2020★ 1 cited

Driver Identification through Stochastic Multi-State Car-Following Modeling

Donghao Xu, Zhezhang Ding, Chenfeng Tu +4

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. In this study, a joint model of the two types of heterogeneity i…