Advanced adaptive cruise control based on operation characteristic estimation and trajectory prediction

Hanwool Woo, Hirokazu Madokoro, Kazuhito Sato, Yusuke Tamura, Atsushi Yamashita, Hajime Asama

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number4875
JournalApplied Sciences (Switzerland)
Issue number22
Publication statusPublished - 2019 Nov 1


  • Adaptive cruise control
  • Autonomous driving
  • Operation characteristic estimation
  • Trajectory prediction


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