This paper proposes a novel approach to measuring human driving performance by using the AI capabilities of automated driving systems, illustrated through three example scenarios. Traditionally, the assessment of human driving has followed a bottom-up methodology, where raw data are compared to fixed thresholds, yielding indicators such as the number of hard braking events. However, acceleration threshold exceedances are often heavily influenced by the driving context. We propose a top-down context-aware approach to driving assessments, in which recordings of human-driven vehicles are analyzed by an automated driving system. By comparing the human driver’s speed to the AI’s recommended speed, we derive a level of disagreement that can be used to distinguish between hard braking caused by aggressive driving and emergency braking in response to a critical event. The proposed method may serve as an alternative to the metrics currently used by some insurance companies and may serve as a template for future AI-based driver assessment.

A spectrum of control methods in human–robot interaction was investigated, ranging from direct control to telepresence with a virtual representation of the robot arm. A total of 24 participants used a setup that included a Franka Emika Panda robot arm, Varjo XR-3 head-mounted display, and Leap Motion Controller. Participants performed a box-and-block task using the bare hand (A), and under five gesture-controlled robotic operation methods: direct sight (B), sight via video-feedthrough (C), in a 3D telepresence environment with (D) and without (E) virtual representation of the robot arm, and using a 2D video feed (F). The number of grabbing attempts did not differ significantly between conditions, but local operation (B & C) yielded more transferred blocks than teleoperation (D–F). Teleoperation using a 3D presentation was advantageous compared to teleoperation using a 2D video feed, as demonstrated by lower peak forces and smaller range in gripper heights in conditions D and E compared to condition F, a finding supported by analyses of the head movement activity. Finally, the bare hand yielded the best performance and subjective ratings. In summary, teleoperation using a 3D presentation provided a smoother interaction than teleoperation with a 2D video feed. However, direct human interaction remains a benchmark yet to surpass.