# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 6 Conclusions  
The TAKEN system has implemented various components of an SDC namely, visual perception, state estimator, motion planning and behavioural modelling, using the Carla simulator. The TAKEN system has successfully devised an alternative solution to handle issues faced by self-driving agents due to sensor failures. In addition to this, the work has also introduced how one could infer knowledge from other players in the environment so as to make wiser and quicker decisions. The main motivation behind introducing this concept was from papers such as \"Crazyswarm: A large nano-quadcopter swarm\" [33] in which the quadcopters collectively estimate state and performs trajectory planning. The drawback of this work is that the knowledge-sharing module has been trained only on a subset of a class--pothole images. Because the training is performed with images and not videos, historical information about series of actions and situations has not been accounted for. We look forward to improving the current work's perception stack by considering these issues. We plan on achieving this by extracting the information from the latent space of the time-series-action-pair data (that is, a pair of the situation and the corresponding action over time) and estimating the expected action of the player. Deep Reinforcement learning is an unsung branch of artificial intelligence with the potential to help us implement a solution for this problem. This concept will allow the model to learn on its own by exploration and exploitation. We will also be working on scaling up the communication architecture discussed in the TAKEN system and methodology section by providing additional functionalities so that it can aid vehicles in different complex scenarios. Building accurate models with minimal resource consumption is an open challenge in this work. The result of this work is recorded as a video and can be viewed by  
Figure 11: Estimation results by the TAKEN model with object and depth estimation in (**A**) and depth and sector estimation in (**B**).  
Figure 12: Road condition estimation.  
opening the following custom link. [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) IXyGhBM2 OLqZ54 HTRtfPyoeYW-f11a1/view?usp=sharing]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) IXyGhBM2 OLqZ54 HTRtfPyoeYW-f11a1/view?usp=sharing) (accessed on 30 October 2022).  
Conceptualization, N.K.B. and R.F.; methodology, N.K.B., R.F. and A.P.R.; software, N.K.B.; validation, A.P.R., T.R.G. and P.V.; formal analysis, M.M., T.R.G.; investigation, M.M.; resources, R.F.; data curation, A.P.R.; writing--original draft preparation, N.K.B. and R.F.; writing--review and editing, M.M., T.R.G.; visualization, A.P.R., M.S.K. and P.V.; supervision, R.F. and M.M.; project administration, M.M., T.R.G. and M.S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[PERSON] is supported by the AI-TOP (2020-1-UK01-KA201-079167) and DIVERSIA (618615-EPP-1-2020-1-UKEPPKA2-CBHEJP) projects funded by the European Commission under the Erasmus+ programme.  
The authors declare no conflict of interest.