This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Computation of Driving Pleasure based on Driver's Learning Process Simulation by Reinforcement Learning
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 25, 2013 by SAE International in United States
Annotation ability available
In order to improve the driver's experiences such as driving pleasure, it is important to evaluate the relationship between various vehicle characteristics and the driver's feeling. Although methods such as sensory subjective evaluation are commonly used, the mechanism behind them is not yet fully understood.
In this paper we introduce a novel method for evaluating driving pleasure based on the numerical simulation of the driver's learning process. As an example of this method we evaluate the relationship between mechanical property of steering system and pleasure felt during the driver's learning process.
One possible method to simulate the driver's learning process is machine learning. Reinforcement learning has been studied for simulating the human's brain function to learn . We use machine learning to create the reinforcement learning driver model, and a simple vehicle simulation model which are combined as a human-vehicle model. Then the model, with four different settings of steering stiffness, is simulated to learn to drive on a winding road constructed with two curves. The result shows that the characteristics of driver's learning process depend on the steering stiffness. We also find that there is a trade-off between the learning speed at the beginning and the learned level at the end of the learning process. So we estimate there is an optimal steering stiffness for continuous progress while learning how to drive, with which the driver can feel a high sense of accomplishment.
The aim of this research is to investigate whether the driver's progress process can be simulated or not. So in this study, we used the simple vehicle and driver model. We will continue to develop more precise models of both vehicle and driver to unearth the mechanisms of driving pleasure.
|Technical Paper||The Use of Simulation in Truck Safety Research, Driver Training and Proficiency Testing|
|Technical Paper||Smart On-Street Parking System to Predict Parking Occupancy and Provide a Routing Strategy Using Cloud-Based Analytics|
CitationSakuma, T., Shimizu, T., Miki, Y., Doya, K. et al., "Computation of Driving Pleasure based on Driver's Learning Process Simulation by Reinforcement Learning," SAE Technical Paper 2013-01-0056, 2013, https://doi.org/10.4271/2013-01-0056.
- Doya , K. 1996 Temporal difference learning in continuous time and space Touretzky D. S. , Mozer M. C. , & Hasselmo M. E. Advances in neural information processing systems 8 1073 1079 Cambridge, MA MIT Press
- Kuge , N. , Yamamura , T. , Shimoyama , O. , and Liu , A. A Driver Behavior Recognition Method Based on a Driver Model Framework SAE Technical Paper 2000-01-0349 2000 10.4271/2000-01-0349
- Koike Yasuharu , Doya Kenji Multiple state estimation reinforcement learning for driving model -Driver model of Automobile IEEE International Conference on Systems, Man, and Cybernetics V 504 509 1999
- Mizuma , H. , Kuriki , K. et al. Study for the improvement of operation feeling drive control system JSAE Conference 2004 JAPAN May 2004
- Sakuma , T. , Abe , M. , Sato , H. , Shimoyama , O. Characteristics Evaluation of the Variable Gear Ratio Steering system based on the Human's Sensory Characteristics JSAE Conference 2006 JAPAN May 2006
- Abe , M. , Sakuma , T. , Sato , H. , Shimoyama , O. Manufacturing Experimental Vehicle for Measuring Driver's Steering Characteristics JSAE Conference 2006 JAPAN May 2006
- Shimizu , T. , Hirose , S. , Tsunashima , H. et al. Measurement of Frontal Cortex Brain Activity Attributable to the Driving Workload and Increased Attention SAE International Journal of Passenger Cars-Mechanical Systems October 2009 2 1 736 744 10.4271/2009-01-0545
- Schultz W , Apicella P , Ljungberg T Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task J Neurosci 1993 13 900 913
- Sheridan , T.B. Three models of preview control IEEE Trans. on Human Factors in Electronics, HFE 7 2 91 102 1966
- Rusmussen , J. Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other distinctions in Human Performance Models IEEE Trans. Syst. Man & Cybern. 13 3 1983 257
- Barto , A. G. , Sutton , R. S. , & Anderson , C. W. 1983 Neuronlike adaptive elements that can solve difficult learning control problems IEEE Transactions on Systems, Man, and Cybernetics 13 834 846
- Doya K. 2000 Reinforcement learning in continuous time and space Neural Computation 12 219 245
- Schultz , W , Dayan , P & Montague , PR. 1997 A neural substrate of prediction and reward Science 275 5306 1593 1599
- Schweighofer , N. , Tanaka , S. C. , Doya , K. 2007 Serotonin and the evaluation of future rewards: Theory, experiments, and possible neural mechanisms Annals of the New York Academy of Sciences 14 289 300
- ABE Masato Automotive Vehicle Dynamics (Japanese) Tokyo Denki University publication office 978-4501419202 2012