# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Safety Applications
#### 3.1.1 Red-Light-Violation Warning (RLVW)  
In this application, a micro-cell/RSU sends both signal timing and the geometry of the intersection in signal-phase-timing (SPAT) and MAP messages, respectively. The vehicle uses this information along with its speed and acceleration to determine if it is likely to violate the red signal or not [73]. For example, a warning is issued to the driver if the vehicle is approaching an intersection at the start of the yellow signal and the time needed to cross the intersection is longer than the duration of the yellow signal [74].  
Various studies have been carried out to enhance and improve the RLVW application performance. Prior knowledge of red-light-running violations can help in detecting such incidents and in notifying nearby vehicles in advance to take proper actions. In [75], the authors came up with a system model that can predict red-light-running (RLR) to assist in diminishing the fatal accidents caused by such an action. Their proposed model embraces the use of time-of-flight (ToF)-based light detection and ranging (LIDAR) sensors for speed calculations along with a prediction algorithm that is dependent on computing the stop sight distance (SSD) formula to determine the braking distance with respect to the speed. LIDAR positioned at the side and front of the vehicle is able to determine and measure the speed of vehicles approaching the intersection and hence is able to anticipate the RLR violation that the vehicle might experience. The traditional TOF LIDAR sensor technique is widely adopted and is a popular approach for speed/distance estimation due to its simplicity, precision, and sensitivity in comparison to other proximity-based sensing techniques [76; 77]. However, this technique can still suffer from complex design during detection due to high amplification factors and frequency rates [77]. [PERSON] et al. [78] describe a probabilistic stop-or-go prediction framework that depends on Bayesian networks (BN). The authors broaden the deterministic output into a probabilistic output. The BN framework is adjusted and assessed through the streaming trajectories of data gathered by the radar sensors that are employed in the signalized intersection. Many factors are incorporated in their model including the dynamic movement of the vehicles such as speed and accelerations, in addition to considering car-following behavior. Their probabilistic RLR BN prediction framework provides a high accuracy level, and the model's performance is enhanced with feature interpretation. In [74], the impact of voice warnings on the brake response time (RT) to RLR violations in the presence of collision avoidance process was analyzed. The analysis was conducted and tested on a driving simulator. To test the effectiveness of their system warning status, lead time and content were all taken into consideration. Their experiment demonstrates that using voice warnings was able to reduce the RT; hence, the collision occurrence rate was also reduced. Both early and late warnings were able to reduce the RT and the collision occurrence rate, with better results achieved with early warnings. The authors point out that their results can help designers of the RLR collision warning systems to improve the effectiveness of those systems [74]. In their experimental research study, the authors incorporated several demographic factors, such as driver's age, gender, and experience. However, the driver's style and uncertain behavior can also have a significant impact on the braking response time and the RLR violations, which may lead to unsafe signalized intersections [79].