On one approach to the development of a simulator of the movement of an autonomous vehicle with training


  • V. Zaytsev
  • Ye. Bulatetskyi


simulator, Unity 3D, feed-forward neural network, activation functions, GitHub platform


The article deals with the use of simulators for controlling the movement of an autono-mous vehicle and development of a new simulator. The approach to creating a simulator of motion of a vehicle in the C# programming language is described. In the development for the implementation of simulation scenes used Unity 3D multi-platform tool is used in the devel-opment. The simulation uses direct propagation neural network that does not have a clear number of input level neurons, having only a constant output level, consisting of two neurons: the first one is responsible for acceleration, the second one is responsible for the ability of the car to turn left or to the right. Also, there is no clearly defined number of hidden levels and neurons located there. All this data in the simulation can be determined by the user. The input to the neural network values received from lasers. The lasers measure the distances to obstacles and feed the values to the input of the neural network. A sigmoidal activation function is implemented. To train the neural network an augmented learning algorithm is used, namely, a genetic algorithm applied to each vehicle, starting with the creation of each vehicle's own list of genes. In the network of each vehicle the number of genes is equal to the number of weights. For the first generation, the weights are set randomly. For the simulation, a generalized neural network with a large number of settings, with it is possible to change its structure: it is possible to change the number input level neurons that depend on the number of lasers at vehicle, their range, the height at which they detect interference, field of their visibility; you can change the number of hidden levels and the number of neurons that will be located there; control the mutation process used in the genetic algorithm; define the value of the mutation and the range of variation at which values can be varied; turn on and off the self-preservation, change vehicle speed, acceleration, set the maximum and minimum speed, edit the parameters responsible for the rotation of the car and its smoothness. Implementation of the project is provided on GitHub. The simulator can be downloaded by any developer from GitHub and can be used to implement and test various neural network training algorithms, including work of your own design.


Mercedes-Benz Innovation Vehicle Developing. https://www.mercedes-benz.com/en/mercedes-benz/next/advancedengineering/ [Electronic resource]– Ac-cess mode: 22.07.2018.

Emanuele Obialero. A Refined Vehicle Dynamics Model for Driving Simula...rs // Charhalmers University of Technology/Göteborg, Sweden 2013. Master’s thesis,

P. 120.

Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, JeanLuc Gaudiot. «Creating Autono-mous Vehicle Systems». Morgan & Claypool Publishers, 2018. - P. 191

Closed Simulator NVIDIA DRIVE Sim. https://developer.nvidia.com/drive-sim-early-access-program.

Open Source Simulator Carla. CARLA Simulator.

Muhaylov В.G. О nekotopukh podkhodakh modelirovaniya avtomobilya na cimylyatorakh // Sistemnui analiz i prikladnaya informatika. №3, -2019. - с. 29-35.