• Corpus ID: 49293178

Conditional Affordance Learning for Driving in Urban Environments

@article{Sauer2018ConditionalAL,
  title={Conditional Affordance Learning for Driving in Urban Environments},
  author={Axel Sauer and Nikolay Savinov and Andreas Geiger},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.06498}
}
Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. [] Key Result In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation.

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References

SHOWING 1-10 OF 46 REFERENCES

End-to-End Driving Via Conditional Imitation Learning

This work evaluates different architectures for conditional imitation learning in vision-based driving and conducts experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area.

Driving Policy Transfer via Modularity and Abstraction

This work presents an approach to transferring driving policies from simulation to reality via modularity and abstraction, inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches.

End to End Learning for Self-Driving Cars

A convolutional neural network is trained to map raw pixels from a single front-facing camera directly to steering commands and it is argued that this will eventually lead to better performance and smaller systems.

Deep learning algorithm for autonomous driving using GoogLeNet

The proposed deep learning based algorithm is referred to as GoogLenet for Autonomous Driving (GLAD), and it uses only five affordance parameters to control the vehicle as compared to the 14 parameters used by prior efforts.

Off-Road Obstacle Avoidance through End-to-End Learning

A vision-based obstacle avoidance system for off-road mobile robots that is trained from end to end to map raw input images to steering angles and exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.

CARLA: An Open Urban Driving Simulator

This work introduces CARLA, an open-source simulator for autonomous driving research, and uses it to study the performance of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end to-end models trained via reinforcement learning.

Autonomous driving in urban environments: Boss and the Urban Challenge

This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.

ALVINN: An Autonomous Land Vehicle in a Neural Network

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following that can effectively follow real roads under certain field conditions.

End-to-End Learning of Driving Models from Large-Scale Video Datasets

This work advocates learning a generic vehicle motion model from large scale crowd-sourced video data, and develops an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state.

3D Traffic Scene Understanding From Movable Platforms

A novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene is presented.