Moritz Körber

Electric bicycles are an emerging means of transportation. Especially people with physical limitations benefit from the added motor power to propel the bicycle. Conventional motor assistance strategies pose limits on the usability of these bicycles: they lack the flexibility to adapt to the individual physiological constitution of cyclists and do not properly manage the assist power over an entire trip. In this paper, we present a novel energy management system for human-electric hybrid vehicles that: 1) uses an optimal control approach to regulate the heart rate of the cyclist and 2) incorporates trip information to manage the motor assistance. The system consists of a control stage and a planning stage. In the control stage, a model predictive controller regulates the heart rate by changing the motor power and gear ratio to maintain a user-defined exertion while considering constraints. The planning stage processes a priori information about the user and the route to estimate the power demand during different sections of the trip and to calculate the optimal motor power for each section. Motor power constraints for each section are then formulated to limit the energy consumption and to save energy for those sections when motor power is most needed. We present simulation results to demonstrate that: 1) the combined control of motor power and transmission ratio is superior to using each control variable separately and 2) including a trip information helps to manage the energy consumption over the entire trip to decrease the risk of running out of energy. This system can help people with limited physical capabilities to safely engage in physical activity.

Objective: It was investigated whether providing an explanation for a takeover request in automated driving influences trust in automation and acceptance. Background: Takeover requests will be recurring events in conditionally automated driving that could undermine trust as well as acceptance and, therefore, the successful introduction of automated vehicles. Method: Forty participants were equally assigned to either an experimental group provided with an explanation of the reason for a takeover request or a control group without explanations. In a simulator drive, both groups experienced three takeover scenarios that varied in the obviousness of their causation. Participants rated their acceptance before and after the drive and rated their trust before and after each takeover situation. Results: All participants rated acceptance on the same high level before and after the drive, independent of the condition. The control group’s trust ratings remained unchanged by takeover requests in all situations, but the experimental group showed decreased trust after experiencing a takeover caused by roadwork. Participants provided with explanation felt more strongly that they had understood the system and the reasons for the takeovers. Conclusion: A takeover request did not lower trust or acceptance. Providing an explanation for a takeover request had no impact on trust or acceptance but increased the perceived understanding of the system. Application: The results provide insights into users’ perception of automated vehicles, takeover situations, and a fundament for future interface design for automated vehicles.

Changing industrial, social, and legal situations cause a rethinking of novel production systems. One possible approach is to assist the human workers' power. By application of collaborating robots, weaknesses can be compensated by the strengths of the interacting partner using haptic shared control. To ensure acceptance and well-being on the human side, which can be seen as a condition for high performance, usability of the collaborative systems has to be maximized. This study aims to evaluate usability measures for haptic Human-Robot Collaboration in horizontal manipulation tasks (HMT). Results show significant differences and strong effects for accuracy (standard deviation lateral position, SDLP) and efficiency (time to task completion, TTC) measures in a Human Dyad experiment using a Human-Human Collaboration (HHC) method.

Objective: The aim of this study was to quantify the impact of traffic density and verbal tasks on takeover performance in highly automated driving. Background: In highly automated vehicles, the driver has to occasionally take over vehicle control when approaching system limits. To ensure safety, the ability of the driver to regain control of the driving task under various driving situations and different driver states needs to be quantified. Methods: Seventy-two participants experienced takeover situations requiring an evasive maneuver on a three-lane highway with varying traffic density (zero, 10, and 20 vehicles per kilometer). In a between-subjects design, half of the participants were engaged in a verbal 20-Questions Task, representing speaking on the phone while driving in a highly automated vehicle. Results: The presence of traffic in takeover situations led to longer takeover times and worse takeover quality in the form of shorter time to collision and more collisions. The 20-Questions Task did not influence takeover time but seemed to have minor effects on the takeover quality. Conclusions: For the design and evaluation of human–machine interaction in takeover situations of highly automated vehicles, the traffic state seems to play a major role, compared to the driver state, manipulated by the 20-Questions Task. Application: The present results can be used by developers of highly automated systems to appropriately design human–machine interfaces and to assess the driver’s time budget for regaining control.

A vehicle automation makes it possible to hand over the control task from the driver to an automation, which is in turn monitored by the driver. In this study, it was investigated how long it takes for drivers to detect an automation malfunction and if the detection time can be predicted by performance in a self-developed vigilance task and by individual boredom proneness. 23 participants drove with partial automation (Level 2; [1, 2]) activated for 24 min on a three-lane highway. Dependent measure was the time until they detected a suddenly occurring malfunction of lateral control. No significant relationship between the predictors and detection time was found, which could be caused by the chosen operationalization of the dependent variable.