103,478 research outputs found
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning
This paper concerns automated vehicles negotiating with other vehicles,
typically human driven, in crossings with the goal to find a decision algorithm
by learning typical behaviors of other vehicles. The vehicle observes distance
and speed of vehicles on the intersecting road and use a policy that adapts its
speed along its pre-defined trajectory to pass the crossing efficiently. Deep
Q-learning is used on simulated traffic with different predefined driver
behaviors and intentions. The results show a policy that is able to cross the
intersection avoiding collision with other vehicles 98% of the time, while at
the same time not being too passive. Moreover, inferring information over time
is important to distinguish between different intentions and is shown by
comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a
Deep Q-learning at 1.75%.Comment: 6 pages, 7 figures, Accepted to IEEE International Conference on
Intelligent Transportation Systems (ITSC) 201
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
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
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