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This paper focuses on the development of the centralized air flow system S-FLOW (Energy Saving Air Flow Control System). The S-FLOW system directs thermal energy to each seating position in the vehicle based on occupancy, thus prioritizing the energy usage based on the particular scenario. The thermal environment in a vehicle's cabin is non-uniform. If the climate control system is used to direct airflow exclusively to any one region of the cabin, without special considerations, comfort may be adversely impacted. To solve this concern, a non-uniform evaluation method was developed to evaluate comfort at each body region of the occupant using the SET* (Standard new effective temperature) method. SET* is a parameter that combines the effects of temperature, airflow velocity, humidity, and other parameters to quantify thermal comfort. Next, a method was established that correlated each body region's SET* value to the occupant's overall thermal comfort.

As the deployment of automated vehicles (AVs) on public roadways expands, there is growing interest in establishing metrics that can be used to evaluate vehicle operational safety. The set of Operational Safety Assessment (OSA) metrics, that include several safety envelope-type metrics, previously proposed by the Institute of Automated Mobility (IAM) are a step towards this goal. The safety envelope OSA metrics can be computed using kinematics derived from video data captured by infrastructure-based cameras and thus do not require on-board sensor data or vehicle-to-infrastructure (V2I) connectivity, though either of the latter data sources could enhance kinematic data accuracy. However, the calculation of some metrics includes certain vehicle-specific parameters that must be assumed or estimated if they are not known a priori or communicated directly by the vehicle.

The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed. A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations.