# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 8 Conclusions  
This paper has explored the use of a potential reputational management system for autonomous vehicles and employed an experimental simulation system to test the components of this reputation management system. From the simulation, it was shown that reputation management can be a reliable method to identify malicious behavior in autonomous vehicles. Figure 5 depicts the stark contrast in operating and reporting scores between vehicles that were classified as malicious and normal. Notably, the model provided additional nuance by measuring both the operating and reporting score of vehicles, as opposed to using a single metric for reputation. Parameters such as the proportion of witnesses (in Figure 8) exhibited a minimal change in operating error, but an increase in reporting error, with more witnesses; these findings would not have been captured if operating and reporting scores were conflated.  
From this work, several conclusions, pertinent to constructing a robust AV reputation management system, can be drawn. First, accounting for the reputation of witnesses is imperative in determining the credibility of transactions. Enabling this functionality resulted in a dramatic decrease in both operating and reporting error, as evidenced in Figure 9. Systems must be scalable and perform adequately in larger populations, which the proposed system was demonstrably able to accomplish. There was a noticeable decrease in operating error, as there were more vehicles in the simulation, whose results are presented in Figure 11. While the model performed well, under realistic conditions of having 10% of participating vehicles as malicious and 90% as normal, Figure 7 shows how its performance deteriorated as the proportion of malicious vehicles increased.  
Several other parameters were shown to have a minimal impact on the efficacy of the system. The weighting of recent transactions and number of witnesses, as shown in Figures 6 and 10, both resulted in a negligible change in error. The recency of transactions did not produce a significant change because vehicle behavior was relatively consistent throughout the whole simulation, as a consequence of vehicles' status as malicious or normal remaining static. Vehicles initialized as normal and malicious remained normal and malicious, respectively, for the entire simulation. Therefore, the credibility of a vehicle's transactions towards the end of the simulation was not different from their transactions earlier. The number of witnesses also did not play an impactful role in the calculation of  
Figure 11: Average operating and reporting error from increasing the number of vehicles in the simulation.  
vehicle reputation, since transactions with few witnesses were invariably balanced out by transactions with a greater number of witnesses.  
Notably, the simulation did not reflect malicious group behaviors, such as vehicle-herding, where a few malicious vehicles travel together to attempt to harm the reputation of other vehicles. Had the simulation included such behaviors, the number of witnesses may have been more impactful in weighting the credibility of transactions.  
The current limitations of autonomous vehicle research, due to the absence of full-scale autonomous vehicle environments in the real world, have made it difficult to identify a consensus on the model parameterization. While many of the simulation settings were varied to determine the most optimal parameters, other settings, such as the concentration of malicious vehicles, require more research to determine realistic values.  
It is important to note that the proposed system is most effective when all autonomous vehicles on the road are participating. However, this is more of a policy problem because establishing a widely accepted security protocol for autonomous vehicles would contribute to the growth of their use. The system will still be somewhat effective if it is not adopted by all vehicles (lower population), based on the data from Figure 11.