# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 3 System Architecture and Design
### System Environment and Operational Scenario  
In delineating the task environment for a state-of-the-art SAE Level 4 AV, we introduce a structured specification termed performance, environment, actuators, and sensors (PEAS), as elaborated in Table 4. This framework serves as a foundational guide for comprehending and tackling the complexities inherent in the AV's operational milieu. The PEAS framework facilitates a methodical categorization of the AV's environment across critical dimensions, aiding in the selection of appropriate agent designs and implementation techniques specifically tailored for highly autonomous driving scenarios. Specifically, for the AV environment, the following information is central:  
Given the complexity of road systems and the diverse array of factors influencing AV operation, it becomes imperative to categorize the task environment to design effective agents and implement suitable techniques. The environment in which AVs operate is partially observable, as they cannot fully predict how other road users will behave. Moreover, it is inherently multi-agent, as vehicles must navigate alongside various other road users, each with their own goals and behaviours. Additionally, the environment is stochastic, meaning AVs must contend with probabilities rather than certainties regarding events like traffic flow and pedestrian movement. Finally, the sequential nature of decision-making in this environment underscores the importance of recognizing that short-term decisions made by AVs can have significant long-term consequences, emphasizing the need for robust planning and adaptability in agent design.  
Therefore, the task environment should be categorised in terms of its dimensions to determine the most appropriate agent design and identify suitable techniques for implementing the agent. For the AV, the environment is as follows:  
* Partially observable--the vehicle cannot sense how other road users will behave.
* Multi-agent--the vehicle is competing with other road users to achieve its goals.
* Stochastic--the vehicle is dealing with probabilities of events occurring, not certainties.
* Sequential--short-term decisions made by the agent can have long-term consequences.  
Following the discussion on agents and ADASs in the previous section, a system architecture can be proposed for an AV that can define the operational scenarios as follows:  
* Operates at SAE Level 4 under limited conditions (e.g., highway driving).
* Acts rationally, with scope for learning (e.g., user preferences).
* Supports human drivers outside of limited conditions (e.g., off-road driving).
* Is part of a multi-agent network, communicating and negotiating with other road users.
* Responds to hierarchical control from a coordinator, linking road users to the road infrastructure.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Agent** &
\begin{tabular}{l} **Performance** \\ **Measure** \\ \end{tabular} & **Environment** & **Actuators** & **Sensors** \\ \hline \multirow{8}{*}{SAE Level 4} & Punctuality & Road Type & Throttle & Lidar \\  & Safety & Road Users & Brake & Radar \\ \cline{1-1}  & Comfort & Pedestrians & Steering & GPS \\ \cline{1-1}  & Fuel Economy & Weather & Indicators & Cameras \\ \cline{1-1}  & Driving Style & Laws & Horn & Telemetry \\ \cline{1-1}  & Law Abiding & & Lights & Odometry \\ \cline{1-1}  & & & Cabin Controls & Microphone \\ \cline{1-1}  & & & & Infotainement \\ \cline{1-1}  & & & & System \\ \hline \hline \end{tabular}
\end{table}
Table 4: PEAS framework description for SAE Level 4 autonomous vehicles.