# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 5 Results  
Figure 10A,B represent the implication of using the Stanley controller on the simulated car model. This module makes use of the navigation system to identify its current position in the environment and thus estimates the steering angle. Figure 10A represents a set of waypoints that a model must take to reach the destination and Figure 10B represents the path taken by the simulated car guided by the Stanley controller, both in terms of the map coordinate system. The effect of using the PID controller is shown in Figure 10E. The red-coloured curve represents the reference speed and the blue-coloured curve represents the actual speed measured with respect to simulator time in seconds vs speed in kmph. In case of sensor failures or erroneous sensors, the control shifts to the AI state estimator which is used for the identification of the current position in the environment. The aforementioned modules are run in parallel so as to keep them synchronised. Figure 10C,D are the outputs of the AI state estimator obtained during the execution. Figure 10C represents the velocity that a vehicle must use as a reference to define the throttle position. On the other hand, Figure 10D shows how the vehicle abides by the designated path. Here the blue points represent the predicted value and red points represent the actual/true value. This is the normalised representation of the Carla environment whose axes are longitude, latitude and velocity/heading respectively. The knowledge sharing module in the TAKEN system uses images as raw information for detecting potholes on the road (Figure 10F) and cautioning autonomous vehicles about road conditions. Figure 10G shows that the autonomous vehicle maintained the correct lane during its course of action. For every time step, if the vehicle maintained the correct lane, the agent is rewarded with value one, otherwise, it is credited with zero rewards. In the execution environment, during the events such as overtaking and intersection crossing, the rewards are considered to be zero. Pretrained Convolution Neural Network (CNN) models such as VGG19, Xception, InceptionV3, MobileNet, ResNet152 and ResNet152V2 were used to identify events. Figure 10H represents the accuracy of each model used to design \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Concepts** & **Existing Works** & **Uniqueness of the TAKEN System** \\ \hline \multirow{8}{*}{AI State Estimator} & It was observed that methods such as RLS and Kalman filters were used to handle erroneous sensors but not their failures. The main advantage of these methods is that they compute the true value of any quantitative entity on the fly. & Usage of neural networks to define paths between any two points and using heading and velocity predictor to estimate its current state in the cases of sensor failure or erroneous sensors. This method enables a system to move between two points without a navigation system. \\ \hline \multirow{4}{*}{Visual Perception} & Most of the existing works used pretrained models trained on Coco/ImageNet dataset to detect objects in the scene. & The TAKEN system uses a custom dataset containing annotations for pedestrians, vehicles, traffic lights and signals and other static objects, defined in the synthetic environment of the Carla simulator. \\ \hline \multirow{4}{*}{Knowledge Sharing Module} & This ideology is absent in most of the existing work. & The work proposes a centralised–decentralised architecture for communicating among other agents and cloud services in the environment to enhance the process of decision making and reduce computation requirements. \\ \cline{1-1} \cline{2-3}  & This ideology is absent in most of the existing work. & The work proposes a centralised–decentralised architecture for communicating among other agents and cloud services in the environment to enhance the process of decision making and reduce computation requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the TAKEN system with other existing works.