# Brake Light Detection Algorithm for Predictive Braking
## 6 Conclusions  
This paper presented a lightweight algorithm for brake light detection built by combining existing YOLOv3 object detection and scikit-learn random forest classifier implementations. Conducted experiments yielded promising results, delivering a high prediction accuracy even up to distances as far as 150 m. The hybrid approach of colorspace pre-processing and a simple random forest classifier showed a notable improvement when compared to processing with only a random forest classifier. When analyzing the performance of the algorithm, it was remarked that most false readings were caused by light reflections and rapidly changing lighting conditions.  
The method presented could be used to improve many other assistance systems in vehicles, such as adaptive cruise control and possibly collision avoidance. In suburban areas, detecting brake lights could be used as a part of brake assist. In both cases, the best result would be achieved with a sensor fusion of camera and radar data.  
Future research will include utilizing the communication interface of the research vehicle for tuning the adaptive cruise controller with the output of a real-time implementation of the brake light detection algorithm. The goal is to improve the ACC performance in real world field tests by improving the longitudinal control and more accurately anticipating the preceding vehicle intentions than the current radar-based system installed in the research vehicle. The radar-based ACC can only tell the current speed and heading of the object with limited information about the future intentions of the vehicle. Utilizing machine vision to detect the braking status of the vehicle ahead adds another layer for control, thus potentially rendering the ACC more predictive. The predictive nature in the control system would improve the overall safety and comfort levels of passengers in the vehicle. This type of predictive control system should be equipped in future production vehicles for enhanced passenger satisfaction.  
Conceptualization, J.P., R.O., J.V., K.K. and K.T.; Methodology, J.P. and R.O.; Data curation, J.P., R.O. and J.V.; Investigation, J.P. and R.O.; Formal analysis, J.P., R.O., J.V. and K.K.; Software, J.P. and R.O.; Writing--original draft preparation, J.P. and R.O.; Writing--review and editing, J.P., R.O., J.V., K.K. and K.T.; Visualization, J.P., R.O., J.V. and K.K.; Supervision, K.T.; Project administration, J.P., K.T. and R.O.; and Funding acquisition, K.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Henry Ford Foundation Finland, Aalto University and Academy of Finland (Grant Number:326346).  
Not applicable.  
Code used in this study is available at: [[https://version.aalto.fi/gitlab/pirhonj2/brake-light-project](https://version.aalto.fi/gitlab/pirhonj2/brake-light-project)]([https://version.aalto.fi/gitlab/pirhonj2/brake-light-project](https://version.aalto.fi/gitlab/pirhonj2/brake-light-project)) (accessed on 8 March 2022). The recorded dataset used in this study will be publicly available after the publication.  
The authors declare no conflicts of interest.