Abstract
This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.
Highlights
A lot of studies on automatic operation for cars have been actively done by various companies and research groups [1] [2] [3]
This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling
A new obstacle avoidance control method of a car has been developed via just-in-time modeling
Summary
A lot of studies on automatic operation for cars have been actively done by various companies and research groups [1] [2] [3]. The number of the researches on automatic operation techniques from the viewpoint of big data is small, and we can say that these techniques are strongly needed in terms of actual utilization of self-driving. The purpose of this research is to develop a new efficient avoidance control method of encountered moving obstacles for cars based on big data and just-in-time modeling. In [11] [12], the authors have proposed a obstacle avoidance control method for cars based on big data and just-in-time modeling, and have shown that the proposed method can compute a control input that can make a car avoid an encountered obstacle.
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