Name
Abstract | ||
|---|---|---|
The final purpose of Automated Vehicle Guidance Systems (AVGSs) is to obtain fully automatic driven vehicles to optimize transport systems, minimizing delays, increasing safety and comfort. In order to achieve these goals, lots of Artificial Intelligence techniques must be improved and merged. In this article we focus on learning and simulating the Human-Level decisions involved in driving a racing car. To achieve this, we have studied the convenience of using Neuroevolution of Augmenting Topologies (NEAT). To experiment and obtain comparative results we have also developed an online videogame prototype called Screaming Racers, which is used as test-bed environment. |
Year | Venue | Keywords |
|---|---|---|
2005 | CCIA | automated vehicle guidance systems,learning human-level ai ability,artificial intelligence technique,comparative result,online videogame prototype,test-bed environment,final purpose,racing car,human-level decision,augmenting topologies,automatic driven vehicle,machine learning,neuroevolution |
Field | DocType | Volume |
Computer science,Neuroevolution of augmenting topologies,Artificial intelligence,Vehicle guidance,Neuroevolution,Car racing | Conference | 131 |
ISSN | ISBN | Citations |
0922-6389 | 1-58603-560-6 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Gallego | 1 | 3 | 2.58 |
| Faraón Llorens | 2 | 5 | 3.78 |
| Mar Pujol López | 3 | 28 | 8.54 |
| R. Rizo | 4 | 51 | 14.90 |