Automating emergency landing in cities with cognitive stress reducing explanations to pilot-in-the-loop

Abstract

Future smart cities will integrate urban air mobility (UAM) where electric vertical take-off and landing aircrafts (eVTOL) will improve labour and logistic mobility. One of the challenges in urban flight is emergency landing, where an eVTOL needs to land in an unofficial roof top or clear area safely within a time frame. In semi-autonomous eVTOLs, artificial intelligence (AI) is expected to assist in identifying emergency landing locations and navigation vectors, whilst the pilot flies and lands the eVTOL. Autonomously searching and correctly identifying safe candidate locations is important for safety of both the eVTOL and ground stakeholders. Furthermore, the processes of using AI to identify such locations should ideally be explainable to the pilot to reduce cognitive stress and aid the preparation of the emergency landing procedure. Here, we have developed a simulated emergency urban landing platform, whereby an eVTOL rotorcraft is scanning the ground for suitable emergency landing locations and actively explaining the navigation vectors to the pilot-in-the-loop. Cognitive stress is measured using real experiments using heart rate monitor, and an actor-critic deep reinforcement learning is used to learn what explanations are useful to reduce cognitive stress. The end result is that the eVTOL can identify emergency landing candidates, navigate to the safe landing zone (SLZ) whilst performing obstacle avoidance, and explain its decision making to the pilot-in-the-loop whilst minimizing its cognitive stress. We show through 3 scenarios that we can indeed reduce cognitive load significantly and also reduce the average time to reach the SLZ.