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Open AccessArticle

Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

1
Department of Intelligent Mechatronics, Faculty of Systems Science and Technology, Akita Prefectural University, Akita 015-0055, Japan
2
Institute of Engineering Innovation, Graduate School of Engineering, The University of Tokyo, Tokyo 113-0023, Japan
3
Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-0023, Japan
*
Author to whom correspondence should be addressed.
This paper is an extended version of the paper published in the 2018 IEEE International Conference on Intelligent Transportation Systems held in Hawaii, USA, 4–7 November 2018.
Appl. Sci. 2019, 9(22), 4875; https://doi.org/10.3390/app9224875
Received: 21 October 2019 / Revised: 11 November 2019 / Accepted: 12 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Intelligent Transportation Systems)
In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers. View Full-Text
Keywords: autonomous driving; adaptive cruise control; operation characteristic estimation; trajectory prediction autonomous driving; adaptive cruise control; operation characteristic estimation; trajectory prediction
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MDPI and ACS Style

Woo, H.; Madokoro, H.; Sato, K.; Tamura, Y.; Yamashita, A.; Asama, H. Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction. Appl. Sci. 2019, 9, 4875.

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