118 research outputs found
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Today, there are two major paradigms for vision-based autonomous driving
systems: mediated perception approaches that parse an entire scene to make a
driving decision, and behavior reflex approaches that directly map an input
image to a driving action by a regressor. In this paper, we propose a third
paradigm: a direct perception approach to estimate the affordance for driving.
We propose to map an input image to a small number of key perception indicators
that directly relate to the affordance of a road/traffic state for driving. Our
representation provides a set of compact yet complete descriptions of the scene
to enable a simple controller to drive autonomously. Falling in between the two
extremes of mediated perception and behavior reflex, we argue that our direct
perception representation provides the right level of abstraction. To
demonstrate this, we train a deep Convolutional Neural Network using recording
from 12 hours of human driving in a video game and show that our model can work
well to drive a car in a very diverse set of virtual environments. We also
train a model for car distance estimation on the KITTI dataset. Results show
that our direct perception approach can generalize well to real driving images.
Source code and data are available on our project website
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
Evolving a rule system controller for automatic driving in a car racing competition
IEEE Symposium on Computational Intelligence and Games. Perth, Australia, 15-18 December 2008.The techniques and the technologies supporting Automatic Vehicle Guidance are important issues. Automobile manufacturers view automatic driving as a very interesting
product with motivating key features which allow improvement of the car safety, reduction in emission or fuel consumption or
optimization of driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics,
consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. For this work we have designed an automatic controller that learns rules with a genetic algorithm.
This paper is a report of the results obtained by this controller during the car racing competition held in Hong Kong during the IEEE World Congress on Computational Intelligence (WCCI 2008).Publicad
Driving Cars by Means of Genetic Algorithms
Proceedings of: 10th International Conference on
Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad
- …