Towards Group Activity Recognition and Analysis for Improving the Safety of Automated Vehicles

Abstract

Exploiting IoT data collected via embedded sensors in mobile devices and things is useful in context-aware applications. Such context-awareness can also be useful for automated vehicles (AV s) which need to be aware of the surroundings for safety and efficiency reasons. Also, scaling up of Human Activity Recognition (HAR) to Group Activity Recognition (GAR) has at-tracted significant attention recently. One of the crucial elements of every context-aware system is obtaining context data from context providers (CPs) to be able to recognize a group activity (GA) or a situation among a group. In this paper, we extend our previous framework, called GARSAaaS+ (GARSA-as-a-Service+) to interact with external Context Providers to offer services for mobile Group Activity Recognition and Situation Analysis (GARSA) applications in relation to improving the safety for emerging AV s. Also, context-aware data caching in IoT-enabled applications in order to obtain context quickly is an open challenge - in this work, we propose an approach applying caching rules defined using GroupSense-L, which is a specification language for group activities. We demonstrate our proposed middleware via two scenarios to improve group and individual safety when an automated vehicle is involved. We also demonstrate the feasibility of our model and the expressiveness of our proposed model via a range of scenarios.