23 citations · 34 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving
Thibault Buhet, Emilie Wirbel, Andrei Bursuc +1
To navigate safely in urban environments, an autonomous vehicle (ego vehicle) must understand and anticipate its surroundings, in particular the behavior and intents of other road…
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…
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)…
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Maximilian Jaritz, Raoul de Charette, Emilie Wirbel +2
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse dept…