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Screenshots of the crosswalk and roundabout environment; environments were manipulated between subjects. Figures in high resolution are available online (see data availability statement)
Source publication
In recent years, there has been a debate on whether automated vehicles (AVs) should be equipped with novel external human–machine interfaces (eHMIs). Many studies have demonstrated how eHMIs influence pedestrians’ attitudes (e.g., trust in AVs) and behavior when they activate (e.g., encourage crossing by lighting up). However, very little attention...
Citations
... Many traffic situations require communication between interaction partners to coordinate their actions. Communication can happen in various ways and through several modalities [1]- [3]. Implicit communication by means of the vehicle behavior is especially relevant in interactions between pedestrians and automated vehicles (AVs), since driver-focused communication cues such as eye contact are no longer available [2]. ...
... The own behavior inevitably entails some information about the own intention, even if no communication is intended [5]. In [3], the authors argue that not activating an eHMI that informs a pedestrian about the vehicle state or intention can communicate the vehicle's non-yielding intention. In previous work, we demonstrated the possible benefit of communicating the vehicle's intention of giving right of way using a comprehensible eHMI [22]. ...
... Equation (2) denotes the cost function that describes a tradeoff between driving comfort (penalization of j), energy consumption (penalization ofj), and time efficiency (penalization of t e ). Equation (3) assures that the trajectories fulfill the system dynamics and the initial conditions. Equation (4) guarantees the fulfillment of the terminal equality conditions g(x(t e ), t e ) for those end states fixed to desired values at the end of the optimization. ...
In interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning approach that allows to consider the influence of the planned vehicle behavior on a pedestrian and predict a pedestrian's reaction. We plan the behavior by solving two consecutive optimal control problems (OCPs) analytically, using variational calculus. We perform a validation step that assesses whether the planned vehicle behavior is adequate to trigger a certain pedestrian reaction, which accounts for the closed-loop characteristics of prediction and planning influencing each other. In this step, we model the influence of the planned vehicle behavior on the pedestrian using a probabilistic behavior acceptance model that returns an estimate for the crossing probability. The probabilistic modeling of the pedestrian reaction facilitates considering the pedestrian's costs, thereby improving cooperative behavior planning. We demonstrate the performance of the proposed approach in simulated vehicle-pedestrian interactions with varying initial settings and highlight the decision making capabilities of the planning approach.
