Genetically generated bionic driver models for autonomous road vehicles
Typ
Examensarbete för masterexamen
Program
Automotive engineering (MPAUT), MSc
Publicerad
2021
Författare
Gurushanthappa, Shishir
Lindgren, Olle
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
For autonomous road vehicles, control is often divided into longitudinal and lateral
control. This thesis focuses on lateral control driver models derived from a cognitive
perspective. A genetic algorithm is used to generate driver models expressed in a
domain-specific language. The project focuses on isolating perceptual cues. The
objective function for the genetic algorithm is computed as the difference between
the estimated steering angles and the observed steering angles in the vehicle. The
recordings were captured from a Volvo XC90 driving a single scenario with an
S-shaped test track at different speeds, and with different drivers.
The resulting driver models are within 1-2 degrees of the recorded steering angles,
and more significantly, the DSL sentences are very similar regardless of driver or speed,
and stable between different runs. The project’s results show that the implementation
of the genetically generated driver models is possible for lateral control. This genetic
algorithm serves as a platform for the future inclusion of external factors affecting
the dynamics of the vehicle. The identified model and parameters can be tested for
representing a real-world driving case.
Keywords:
Beskrivning
Ämne/nyckelord
autonomous driving , domain-specific language , genetic programming , genetic algorithm , driver models , stochastic optimization , autonomous road vehicles