Name
Abstract | ||
|---|---|---|
This paper studies the performance of a convolutional neural network (CNN) trained to learn the behavior of a vehicle using data from a simulator that allows real-time information gathering from vehicle chassis, machine position and speed. The network uses information from the front-facing, right and right cameras, the car's position on the lane and its speed. This approach proves to be quite effective: with a minimum of driving time taken directly from proper driving simulations in the form of a game, the system learns to drive on a marked strip road. The network automatically learns the internal representations of the necessary processing steps, such as the detection of useful road features, required speed, and track position. Different types of activation functions are used, and it is noticed that the exponential linear unit (ELU) activation function leads to improved learning compared to other activation functions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20257-6_20 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Deep learning,Convolutional neural network,Behavioral cloning,Autonomous driving,Simulator | Convolutional neural network,Activation function,Computer science,Real-time computing,Chassis,Artificial intelligence,Deep learning,Exponential linear units | Conference |
Volume | ISSN | Citations |
1000 | 1865-0929 | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreea-Iulia Patachi | 1 | 1 | 0.37 |
| Florin Leon | 2 | 71 | 15.03 |
| Doina Logofatu | 3 | 17 | 16.74 |