# Brake Light Detection Algorithm for Predictive Braking
## 2 State-of-the-Art  
The aspect of passenger comfort in passenger vehicles has previously garnered significant research interest. This research indicates that multi-directional acceleration affects the passenger's trust and comfort levels, but the longitudinal deceleration is more commonly measured and studied in braking situations. Studies have focused on passenger comfort and anticipated feelings of safety in the existence of ADAS systems [7; 8; 9; 10]. Traditionally, roughly 0.2 g has been considered a critical comfort limit for longitudinal deceleration; anything above the limit is beyond the comfort zone [7]. More recent studies have considered even lower deceleration values to be uncomfortable for passengers [10]. In a study conducted by [PERSON], a passenger tolerance was assessed for longitudinal acceleration and jerk [7]. The study included 11 different cases, including subjective questionnaires as well as objectively measured comfort related parameters. Although there was no conclusive statement, due to a wide variation between the studies and the form of results, it could be seen that normal braking fallings in the range of 0.11-0.15 g would be considered acceptable. [PERSON] et al. [8] proposed a comfort-based vehicle-following model. The model considers  
Figure 1: Concept of using camera feed in addition to radar feed in adaptive cruise control. the comfort aspect by measuring the acceleration to which the passenger is exposed, thus using comfort as control input in the longitudinal deceleration. The results showed that the model is effective and practical for real-world longitudinal deceleration control without sacrificing comfort. [PERSON] et al. [9] studied the impact of a secondary task uptake, such as watching a DVD while driving, in highly-automated vehicle control. The study was performed with 49 driver participants in a simulation environment, each session lasting for 2 h. The conclusion of the study was that automation improves safety margins associated with vehicle following, drivers are more drawn to secondary tasks while the automation levels increase. However, the study also demonstrated that driver fatigue increases with automation. These studies highlight that automated driving tasks are welcomed and that especially control-related tasks need to consider comfort in the design. The importance of considering passenger comfort will further increase in the coming years as partially automated and autonomous vehicles become more common. In this study, brake light detection is realized by a three-stage process involving object detection, image preprocessing, and random forest classification. In further studies, brake light detection will then be used to improve passenger comfort by enabling early braking. This is to ameliorate radar-based ACC by fusing visual information about vehicles braking in front. To the best of the authors' knowledge, no scientific publications regarding this approach exist. However, it is uncertain which technologies are currently being tested and implemented by vehicle manufacturers. The usage of radar in ADAS systems and, more specifically, in measuring the distance to the vehicle driving directly ahead is well established in the industry and researched in academia. [PERSON] et al. [11] focused on the various developmental approaches to radar data processing. In addition, the authors discussed estimation techniques and radar waveform characterization. According to the authors' prognosis, fusing radar data with camera vision, lidar, and other sensors will be more common in the future, as such systems have delivered promising results [12]. In a recent simulation study by [PERSON] et al. [13], a look-ahead scheme of ACC was introduced, predicting the future position and speed of the preceding vehicle by utilizing a conditional persistence prediction technique. The study shows that anticipatory behavior of the ACC could improve the overall driving performance and comfort in high-speed driving.