Abstract
The advance of driving assistance technologies such as Electronic Stability Control (ESC) or auto break system, drivers are released from complicated driving tasks. On the other hand, there is concern that it reduces pleasure feelings of a driver if these system's behaviors are different from the driver's intention. To avoid such problem, it is important to evaluate the driver's intention and decision-making process, and design the assistance system to fit it. Although methods such as sensory subjective evaluation are commonly used, the human cognitive mechanism design behind them is not yet fully understood. In this paper, we introduce a novel method for evaluating driver's decision-making process based on the numerical simulation of the driver's behavior. By using this method, the assistance system can substitute the driver appropriately and driver can accept the system's maneuver because which is same as the driver's intention. As an example of this method we evaluate the relationship between decision-making timing and estimation time length of the driver's model. One possible method to simulate the driver's decision-making is machine learning. Reinforcement learning has been studied for simulating the human's brain function to learn and decide as action and state model. We used machine learning to create the reinforcement learning driver model, and a simple vehicle simulation model which are combined as a human-vehicle model. We used the simple vehicle and driver model because the aim of this research is to investigate whether the driver's decision-making process can be simulated or not. Then the model is simulated to learn to drive on a highway with 3 lanes and other vehicles. The simulated driver made some single lane change to pass a slower vehicle in front or to go out from highway at an interchange. Results showed that the decision-making timing depend on the estimation time of the reinforcement learning model. We exposed that the model behaves similar to general driver's behavior when the estimation time was settled as 7sec which is derived from human brain's cognitive mechanism. In conclusion, our simulation model based on human cognitive mechanism can simulate the driver's lane change decision-making behavior adequately.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 573-578 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538668689 |
| DOIs | |
| Publication status | Published - 2019 Jan 22 |
| Event | 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 - Kandima, Maldives Duration: 2018 Aug 1 → 2018 Aug 5 |
Publication series
| Name | 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 |
|---|---|
| Country | Maldives |
| City | Kandima |
| Period | 18/8/1 → 18/8/5 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Optimization
Cite this
Development of driving intention prediction system based on human cognitive mechanism. / Sakuma, Tsuyoshi; Miura, Satoshi; Miyashita, Tomoyuki; Fujie, Masakatsu G.; Sugano, Shigeki.
2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 573-578 8621765 (2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Development of driving intention prediction system based on human cognitive mechanism
AU - Sakuma, Tsuyoshi
AU - Miura, Satoshi
AU - Miyashita, Tomoyuki
AU - Fujie, Masakatsu G.
AU - Sugano, Shigeki
PY - 2019/1/22
Y1 - 2019/1/22
N2 - The advance of driving assistance technologies such as Electronic Stability Control (ESC) or auto break system, drivers are released from complicated driving tasks. On the other hand, there is concern that it reduces pleasure feelings of a driver if these system's behaviors are different from the driver's intention. To avoid such problem, it is important to evaluate the driver's intention and decision-making process, and design the assistance system to fit it. Although methods such as sensory subjective evaluation are commonly used, the human cognitive mechanism design behind them is not yet fully understood. In this paper, we introduce a novel method for evaluating driver's decision-making process based on the numerical simulation of the driver's behavior. By using this method, the assistance system can substitute the driver appropriately and driver can accept the system's maneuver because which is same as the driver's intention. As an example of this method we evaluate the relationship between decision-making timing and estimation time length of the driver's model. One possible method to simulate the driver's decision-making is machine learning. Reinforcement learning has been studied for simulating the human's brain function to learn and decide as action and state model. We used machine learning to create the reinforcement learning driver model, and a simple vehicle simulation model which are combined as a human-vehicle model. We used the simple vehicle and driver model because the aim of this research is to investigate whether the driver's decision-making process can be simulated or not. Then the model is simulated to learn to drive on a highway with 3 lanes and other vehicles. The simulated driver made some single lane change to pass a slower vehicle in front or to go out from highway at an interchange. Results showed that the decision-making timing depend on the estimation time of the reinforcement learning model. We exposed that the model behaves similar to general driver's behavior when the estimation time was settled as 7sec which is derived from human brain's cognitive mechanism. In conclusion, our simulation model based on human cognitive mechanism can simulate the driver's lane change decision-making behavior adequately.
AB - The advance of driving assistance technologies such as Electronic Stability Control (ESC) or auto break system, drivers are released from complicated driving tasks. On the other hand, there is concern that it reduces pleasure feelings of a driver if these system's behaviors are different from the driver's intention. To avoid such problem, it is important to evaluate the driver's intention and decision-making process, and design the assistance system to fit it. Although methods such as sensory subjective evaluation are commonly used, the human cognitive mechanism design behind them is not yet fully understood. In this paper, we introduce a novel method for evaluating driver's decision-making process based on the numerical simulation of the driver's behavior. By using this method, the assistance system can substitute the driver appropriately and driver can accept the system's maneuver because which is same as the driver's intention. As an example of this method we evaluate the relationship between decision-making timing and estimation time length of the driver's model. One possible method to simulate the driver's decision-making is machine learning. Reinforcement learning has been studied for simulating the human's brain function to learn and decide as action and state model. We used machine learning to create the reinforcement learning driver model, and a simple vehicle simulation model which are combined as a human-vehicle model. We used the simple vehicle and driver model because the aim of this research is to investigate whether the driver's decision-making process can be simulated or not. Then the model is simulated to learn to drive on a highway with 3 lanes and other vehicles. The simulated driver made some single lane change to pass a slower vehicle in front or to go out from highway at an interchange. Results showed that the decision-making timing depend on the estimation time of the reinforcement learning model. We exposed that the model behaves similar to general driver's behavior when the estimation time was settled as 7sec which is derived from human brain's cognitive mechanism. In conclusion, our simulation model based on human cognitive mechanism can simulate the driver's lane change decision-making behavior adequately.
UR - http://www.scopus.com/inward/record.url?scp=85062487025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062487025&partnerID=8YFLogxK
U2 - 10.1109/RCAR.2018.8621765
DO - 10.1109/RCAR.2018.8621765
M3 - Conference contribution
T3 - 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018
SP - 573
EP - 578
BT - 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -