Search Results for author:
Found 8 papers, 2 papers with code
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences.
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving
Our model processes ego vehicle front-facing camera images and bird-eye view grid, computed from Lidar point clouds, with detections of past and present objects, in order to generate multiple trajectories for both ego vehicle and its neighbors.
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
To our knowledge we are the first to present a successful RL agent handling such a complex task especially regarding the traffic light detection.
Ranked #13 on
Autonomous Driving
on CARLA Leaderboard
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment
Mid-to-mid (environment abstraction to mid-level trajectory representation) or direct perception (raw signal to performance) approaches strive to handle more complex, real life environment and tasks (e. g. complex intersection).
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance.
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera
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).
End to End Vehicle Lateral Control Using a Single Fisheye Camera
Experiments are conducted on a custom dataset corresponding to more than 10000 km and 200 hours of open road driving.
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
Ranked #9 on
Depth Completion
on KITTI Depth Completion
