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Found 8 papers, 1 papers with code
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like.
Self-supervised Object Motion and Depth Estimation from Video
We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video.
Learning a Curve Guardian for Motorcycles
Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path.
Learning Accurate, Comfortable and Human-like Driving
Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog).
Ranked #5 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog.
Failure Prediction for Autonomous Driving
This work presents a method to learn to predict the occurrence of these failures, i. e. to assess how difficult a scene is to a given driving model and to possibly give the human driver an early headsup.
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e. g. steering angle and speed) by human drivers.
