Reference no: EM133269883

The project document D.1.22 provides a final specification of IoT-enabled Autonomous Driving (AD) use cases. This is a public report from the project. For this coursework, we focus on the use case "Urban driving with car sharing and city chauffeur services for tourists" described in section 3.2.1, on pages 47-51. The use case describes a storyboard (3.2.1.1) of interaction for a user booking a vehicle from a smartphone app, collecting the vehicle and driving it manually until arriving at a given tourist location (in this case, the Palace of Versailles) and then entering automated driving mode. In this mode the system detects and avoids "VRUs" (vulnerable road users, ie pedestrians and cyclists). On leaving the automated driving zone the user takes back control of the vehicle and returns it to a charging station, validating the trip completion using the smartphone app. Section 3.2.1.2 goes into more detail on the modes and detection technologies, and 3.2.1.3 explains the use of collaborative perception to handle the safety of VRUs using a smartphone app or smart device. As noted in the App Store description for the smartphone app, "The app continuously uses pedestrian's location and sends data to a cloud server. The cloud server collects and manipulates data and provides appropriate notifications to the pedestrian when an AUTOPILOT vehicle or an AUTOPILOT bicycle comes into collision with the pedestrian" the loss of a pedestrian's smart device (for example, a flat battery in the smartphone) can be considered as an initial event for an event tree. Identify any existing steps or barriers that reduce the risks associated with the outcomes. Produce an event tree describing the steps and including these barriers.

Consider additional steps or barriers that could be put in place to further reduce the risk. Describe these barriers, why they would reduce risk and consider how feasible it would be to incorporate them.