Understanding older users’ comfort needs can inform the inclusive design of automated vehicles (AVs). Real-world experience with automated driving is crucial to elicit meaningful insights for older adults, who are expected to benefit from AVs in terms of enhanced mobility and autonomy. In this study, semi-structured interviews were conducted with 27 participants (aged over 60) who experienced a so-called automated ride, operated by a Wizard-of-Oz driver, in Delft, Netherlands. Following the ride, participants were interviewed about their comfort during the ride. Using thematic analysis, we identified three overarching factors associated with user comfort: (1) vehicle factors (including driving styles, AV capabilities, effect of AV exposure , and physical aspects ), (2) environment factors (including effect of external driving environment ), and (3) human factors (including affective experience, attitudes to AV/technology, engagement in non-driving related tasks [NDRTs] , and communication with the AV ). Our findings contribute to understanding comfort in automated driving, by offering a comprehensive list of factors associated with comfort, identifying affective reflections of psychological comfort, and discovering the co-existence of psychological comfort and physical discomfort. The study provides implications for designing comfortable AVs, such as the need for smooth, cautious, and anticipatory driving styles, and flexible and early reactions to unexpected events.

Increasing attention has been drawn to computational pedestrian behavior models aimed at understanding the interaction mechanisms between pedestrians and vehicles. Nevertheless, existing research lacks exploration of the underlying behavioral mechanisms of pedestrian crossing decisions, which leads to unrealistic modeling results. In particular, when dealing with continuous traffic flow scenarios, the concept of waiting time is frequently used to account for all intricate traffic flow effects. Moreover, very few studies considered the time-dynamic nature of crossing decisions. To address these research limitations, this study deconstructs pedestrian crossing decisions at uncontrolled intersections with continuous traffic flow through a cognitive process and proposes an anthropomorphic crossing decision model. Specifically, we propose a novel visual collision cue-based crossing decision-initiation model to characterize time-dynamic crossing decisions. In light of the risk-aversion theory, a traffic gap comparison strategy is put forward to explain and model pedestrian waiting behavior in traffic flow. Two datasets collected from a CAVE-based immersive pedestrian simulator are applied to calibrate and validate the model. The proposed model accurately predicts pedestrian crossing decisions across all traffic scenarios. The modeling performance is significantly enhanced by considering the proposed traffic gap comparison strategy. Moreover, the model accurately captures the timing of crossing decisions. This work concisely demonstrates how pedestrians dynamically adapt their crossings in continuous traffic based on visual collision cues, potentially offering insights into modeling pedestrian-vehicle interactions or serving as a tool to realize anthropomorphic pedestrian crossing decisions in simulators.

Efficient and safe interactions between automated vehicles and other road users can be supported through external Human-Machine Interfaces (eHMI). The success of these interactions relies on the eHMI signals being adequately understood by other road users. A paired-comparison forced choice task (Task 1), and a 6-point rating task (Task 2) were used to assess the extent to which ten different eHMI signals conveyed three separate messages, 'I am giving way', 'I am in automated mode' and 'I will start moving'. The different eHMI options consisted of variations of a 360° lightband, a single lamp, and an auditory signal. Results demonstrated that the same eHMI format could convey different messages equally well, suggesting a need to be cautious when designing eHMI, to avoid presenting misleading, potentially unsafe, information. Future research should investigate whether the use of an eHMI signal indicating a change in the AV's behaviour is sufficient for conveying intention.

Autonomous vehicle control is well understood for local- [15], good approximations exist such as particle ?ltering,ization, mapping and planning in un-reactive environ- which make use of large compute power to draw samplesments, but the human factors of complex interactions near solutions.stood [16], and despite its exact solution being NP-hardwith other road users are not yet developed.Route planning in non-interactive envi-ronments also has well known tractable solutions such asThis po-the A-star algorithm. Given a route, localizing and con-sition paper presents an initial model for negotiation be-trol to follow that route then becomes a similar task totween an autonomous vehicle and another vehicle at anthat performed by the 1959 General Motors Firebird-IIIunsigned intersections or (equivalently) with a pedestrianself-driving car [1], which used electromagnetic sensingat an unsigned road-crossing (jaywalking), using discreteto follow a wire built into the road.Such path follow-sequential game theory. The model is intended as a ba- ing, using wires or SLAM, can then be augmented withsic framework for more realistic and data-driven future simple safety logic to stop the vehicle if any obstacle isextensions. The model shows that when only vehicle po- in its way, as detected by any range sensor.sition is used to signal intent, the optimal behaviors for open source systems for this level of `self-driving' are nowboth agents must include a non-zero probability of al- widely available [6].lowing a collision to occur.In contrast,This suggests extensions toproblems that these vehicles will facearound interacting with other road users are much harderreduce this probability in future, such as other forms ofboth to formulate and solve. Autonomous vehicles do notsignaling and control. Unlike most Game Theory appli-just have to deal with inanimate objects, sensors, andcations in Economics, active vehicle control requires real-maps.time selection from multiple equilibria with no history,They have to deal with other agents, currentlyhuman drivers and pedestrians and eventually other au-and we present and argue for a novel solution concept,meta-strategy convergence , suited to this task.