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.

Understanding driver–pedestrian interactions at unsignalized locations has gained additional importance due to recent advancements in vehicle automation. Naturalistic observations can only provide correlational data of limited value for understanding and modeling the mechanisms underlying road user interaction. Therefore, controlled studies in virtual reality (VR) are an important complement, but conventional methods can only accommodate a single human participant. Recently, there has been some interest in studying interactions in VR, by means of distributed simulation, involving multiple human participants. However, there is a lack of validation of this method. Here, we provide a validation study, focusing on a distributed vehicle–pedestrian interaction setup, where pairs of one driver and one pedestrian interacted under various kinematic conditions in a connected virtual environment. To test the validity of the distributed simulation, we used a naturalistic dataset collected in the same U.K. city, at similar locations, and compared the observed behavior between the two settings. Our results indicate a good relative validity of the simulator study, where road users showed similar nonverbal communication behavior in both datasets. As an additional means of validation, we also leveraged a set of game theoretic models that were developed based on the simulator studies, and found that when applied to the naturalistic dataset, we obtained similar (although not identical) model selection results. The findings suggest that distributed simulation can also be useful for development of computational models of interaction. Overall, the findings suggest that distributed simulation can be a highly valuable tool for studying and modeling road user interactions.

As autonomous driving technology advances, automated vehicles (AVs) will increasingly share road space with pedestrians, creating significant challenges for AV systems. Effective interaction between AVs and pedestrians is one of the key hurdles. Pedestrian simulation tools offer the potential to expedite the evaluation and refinement of these interactive capabilities. However, existing research lacks efforts to model pedestrian behavior in vehicle-yielding scenarios, resulting in distorted modeling results. This paper proposes a perceptually plausible road-crossing decision model that creates temporal-dynamic crossing decisions across a range of vehicle-yielding scenarios. Specifically, a proposed hybrid perception strategy explains how pedestrians may apply psychophysical cues to make crossing decisions. Discrete choice models based on the hybrid perception strategy combined with a crossing initiation model reproduce the details of crossing decisions: the decision and its timing. An empirical dataset collected in a pedestrian simulator is applied to validate the model. Additionally, the latest crossing decision models, i.e., the evidence accumulation model and the artificial neural networks approach, are employed as comparisons. The results show that the proposed model accurately reproduces crossing decision patterns affected by diverse vehicle kinematics in vehicle-yielding scenarios in a perceptually plausible manner. Our results strengthen the notion that there is a perceptual threshold for pedestrians to control their decision-making strategy. The proposed theory and approach bring insights into the computational pedestrian road-crossing behavior and have practical implications in traffic simulation and AV development.

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.

This study investigated drivers' preconceived notions about manoeuvres of Automated Vehicles (AVs) compared to manually driven vehicles (MVs) using a pseudo-coupled driving simulator. The simulator displayed a message indicating the state of approaching vehicles (AV/MV) in a bottleneck scenario, while participants were informed that the MV was controlled by an experimenter using another simulator, despite all trials having the same preprogrammed behaviours. Results showed that the types of AV/MV did not impact participants’ subjective responses. Communication through kinematic cues of the AV/MV was effective, with higher perceived safety, comprehension, and trust reported for approaching vehicles that yielded with an offset away from participants. Perceived safety and trust of the AV were also higher for trials with a light-band external Human Machine Interface (eHMI). This study highlights the value of both explicit and implicit cues for the communication of AVs with other drivers.

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.

The future urban environment may consist of mixed traffic in which pedestrians interact with automated vehicles (AVs). However, it is still unclear how AVs should communicate their intentions to pedestrians. Augmented reality (AR) technology could transform the future of interactions between pedestrians and AVs by offering targeted and individualized communication. This paper presents nine prototypes of AR concepts for pedestrian-AV interaction that are implemented and demonstrated in a real crossing environment. Each concept was based on expert perspectives and designed using theoretically-informed brainstorming sessions. Prototypes were implemented in Unity MARS and subsequently tested on an unmarked road using a standalone iPad Pro with LiDAR functionality. Despite the limitations of the technology, this paper offers an indication of how future AR systems may support future pedestrian-AV interactions.

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.

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.

Anticipating a human collaborator's intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver's intention needs to be an essential part of Advanced Driver Assistance System (ADAS) design. To this end, we propose a framework that exploits drivers' time-series eye gaze and fixation patterns to anticipate their real-time intention over possible future manoeuvres, enabling a smart and collaborative ADAS that can aid drivers to overcome safety-critical situations. The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers' intentions about 3 seconds beforehand with over 90% accuracy.

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.