The anticipated proliferation of small Unmanned Aerial Vehicles (sUAVs) in urban areas has garnered greater interest in capacity estimation of the low-altitude airspace. As a first step to assess such capacity, TRUE lab proposed a topological analysis framework to identify free versus usable airspace in a 3D environment filled with abundant geometric elements.
Autonomous vehicles require algorithms for path planning, static obstacle avoidance, dynamic obstacle avoidance, and many more. Especially, algorithms need to be considered for co-working with human workers to easily adapt the system in existing warehouses with lower setup cost. TRUE lab is developing an optimized algorithm for each robot to find the shortest path from origin to destination for each mission while potential conflicts and deadlocks are prevented.
Transportation data has grown exponentially in the past decades and opened a new channel to analyse and understand numerous common interests in the domain. At TRUE, we utilize multiple data sources for collision analysis and prediction, including feature selection of injury severity prediction, high collision area classification, and post-collision response.
Transportation system is a representative system-of-systems and has been analyzed through interdisciplinary effort to solve various problems including system design, management, and resilience. The rapid increase of traffic data and the emergence of new types of vehicles have increased the accuracy and reliability of the existing analysis methodologies, and at the same time, the necessity of application of novel data-driven methodologies. In particular, the increment of network resolution defined by nodes and links and data collection frequency suggests that the traffic data has already shifted towards the field of big-data analysis.
TRUE lab has been participating in projects that aim to develop risk models to assess risks in the aviation organizations both in quantitative and qualitative ways. Outcomes of past projects include safety data collection guidance for risk analysis and risk assessment, risk assessment framework using a large scale safety data, and state safety indicators.