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.
TRUE lab is participating on a project of development of risk model and related techniques to assess risks in the aviation organizations both in quantitative and qualitative way: direction of collecting safety data for risk analysis and risk assessment, methodology of risk assessment using safety database, and developing state safety indicators. In the Emergency Medical Service (EMS), it is widely recognized that the response time - the time from receipt of an emergency call to arrival at the patient location, highly affects the patient survival rate. TRUE lab proposes a GIS-based method to calculate the k-minute travel time contour to represent the response time coverage, incorporating time-of-day and day-of-week effect on travel time.