System for Hands-on Steering Wheel Detection Using Machine Learning
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Johansson, Emil
Linder, Ravi
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
More cars are getting semi-automatic driving capabilities and on the way towards
full automation, the driver is still responsible for the car. An important aspect of
controlling the car is holding the steering wheel which is supposed to be done at all
times, even during semi-automatic driving. To check that the driver is controlling
the vehicle and holds the steering wheel, a system can be created using inputs from
the steering gear sensors or a camera. Two different solutions using these inputs
are implemented and tested to see if machine learning can be used to differentiate
between hands on and hands off situations. The systems are also evaluated running
online on a Raspberry Pi single board computer where the efficiency of the systems
is considered, as well as performance. The evaluation of the systems shows that
the sensor version can be updated frequently at 5 Hz, while being robust when the
driver is not intentionally tricking the system by hanging a weight on the steering
wheel. The camera model is limited in update frequency by the library used for
running the neural network in C++. The neural network had to be downsized in
order to make the system update frequently enough to detect change of state. This
meant that the system wasn’t able to detect some situations as well as the system
which was evaluated on offline data. Changing libraries for the neural network would
potentially solve this problem and make the online version perform more similarly
to the offline system.
Beskrivning
Ämne/nyckelord
Hands-off detection , Machine learning , State-space model , Kalman filter , Object classification