23 citations · 34 across the 2 of their papers we have counts for
6 papers
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
Adithya Ranga, Filippo Giruzzi, Jagdish Bhanushali +4
Advanced perception and path planning are at the core for any self-driving vehicle. Autonomous vehicles need to understand the scene and intentions of other road users for safe mot…
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette +2
Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In th…
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
Marin Toromanoff, Emilie Wirbel, Fabien Moutarde
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable o…
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment
Thibault Buhet, Emilie Wirbel, Xavier Perrotton
Imitation learning is becoming more and more successful for autonomous driving. End-to-end (raw signal to command) performs well on relatively simple tasks (lane keeping and naviga…
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
Marin Toromanoff, Emilie Wirbel, Fabien Moutarde
Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameter…
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera
Laurent George, Thibault Buhet, Emilie Wirbel +2
In this paper we present a complete study of an end-to-end imitation learning system for speed control of a real car, based on a neural network with a Long Short Term Memory (LSTM)…