text: Pedestrians and vehicles often share the road in complex inner city traffic. This leads to interactions between the vehicle and pedestrians, with each affecting the other's motion. In order to create robust methods to reason about pedestrian behavior and to design interfaces of communication between self-driving cars and pedestrians we need to better understand such interactions. In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior. We propose a LSTM model that takes as input the past trajectories of the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts the future positions of the pedestrian. Our experiments based on a real-world, inner city dataset captured with vehicle mounted cameras, show that the usage of such cues improve pedestrian prediction when compared to a baseline that purely uses the past trajectory of the pedestrian.
  @cite_26: The higher social attention and negotiation among road users while crossing the road is as much a challenge for self-driving cars as it is for pedestrians. Self-driving cars in their current state are not able to understand cues from other road users and are rather reactive to pedestrian behavior, which may result overall in a slower traffic flow. In this paper, a vehicle–pedestrian negotiation model is proposed describing the processing and exchange of negotiation cues from both parties in order to speed up the traffic flow. The motion strategy for the vehicle approaching the pedestrian is formulated in order to negotiate its best chance to pass first, a process that closely mimics the common scenarios of everyday negotiation on roads. Simulation results show an improvement in the overall travel time of the vehicles as compared with the current best practice behavior (always stop) of autonomous vehicles. The cost-benefit analysis of negotiation among both parties is also discussed in this paper.
  @cite_4: The goal of this research is to use agent simulation in realizing human-machine interaction, so that we can perform different aspects of experiments. The domain that we have chosen is the pedestrian-vehicle interaction in street crossing. The participants of the simulation include pedestrians, vehicles, and traffic lights, which are all represented by agents. The autonomous nature along with individual traits of agents enable the simulator to show the dynamic interaction among the agents. The attributes of agents like age, gender, and aggressiveness are considered along with different reaction time. A prototype system uses Jade as the platform in implementation. The experiments show a variety of results based on interaction among various agents.
  @cite_22: Over the last decade, there have been many studies that focus on modeling driver behavior, and in particular detecting and overcoming driver distraction in an effort to reduce accidents caused by driver negligence. Such studies assume that the entire onus of avoiding accidents are on the driver alone. In this study, we adopt a different stance and study the behavior of pedestrians instead. In particular, we focus on detecting pedestrians who are engaged in secondary activities involving their cellphones and similar hand-held multimedia devices from a purely vision-based standpoint. To achieve this objective, we propose a pipeline incorporating articulated human pose estimation, and the use gradient based image features to detect the presence absence of a device in either hand of a pedestrian. Information from different streams and their dependencies on one another are encoded by a belief network. This network is then used to predict a probability score suggesting the involvement of a subject with his her device.
  @cite_41: Cooperative interaction in traffic is vital for resolving a wide range of ambiguities arising from road users' actions. Autonomous vehicles are no exception and require the ability to understand the intention of road users and communicate with them in order to ensure their safety and maintain traffic flow. In this paper, we address the problem of traffic interaction by analyzing a large sample of pedestrians communicating with drivers. We highlight the ways pedestrians communicate and use a logistic regression model to identify what factors influence communication patterns of pedestrians and how. We also discuss practical challenges regarding the recognizing and understanding of pedestrians' intention and how our theoretical findings can help to solve them. Our analysis suggests that pedestrians predominantly rely on implicit communication cues such as stepping onto the road to transmit their intention of crossing. In addition, we found that the presence of traffic signal, street width, and pedestrian group size can influence the frequency and type of pedestrian communication, while factors such as pedestrians' age and gender did not show any significant impact.
  @cite_28: How will pedestrians and bicyclists interact with autonomous vehicles when there is no human driver? In this paper, we outline a novel method for performing observational field experiments to investigate interactions with driverless cars. We provide a proof-of-concept study (N=67), conducted at a crosswalk and a traffic circle, which applies this method. In the study, participants encountered a vehicle that appeared to have no driver, but which in fact was driven by a human confederate hidden inside. We constructed a car seat costume to conceal the driver, who was specially trained to emulate an autonomous system. Data included video recordings and participant responses to post-interaction questionnaires. Pedestrians who encountered the car reported that they saw no driver, yet they managed interactions smoothly, except when the car misbehaved by moving into the crosswalk just as they were about to cross. This method is the first of its kind, and we believe that it contributes a valuable technique for safely acquiring empirical data and insights about driverless vehicle interactions. These insights can then be used to design vehicle behaviors well in advance of the broad deployment of autonomous technology.
  @cite_9: 
  @cite_42: The first autonomous vehicles are already tested in the public traffic. The rapid development in bringing this technology on roads attracts growing attention of research in the human interaction with autonomous vehicles. This paper focuses on the interaction of other road users with autonomous vehicles. These road users may be pedestrians who negotiate their right of way, other human drivers sharing the same road, or human traffic control officers. In order to learn about these road users in general, this paper aims to identify first the formalized hand signals applied by officers. The paper answers the question whether there is a general and universal language to interact with traffic. If so, then future work can identify elements of this universal language in the gestures of other road users, and facilitate an understanding between them and autonomous vehicles.
  @cite_24: In urban environments traffic safety is primarily determined by the successful interaction between road users. In order to define behavioral requirements for future autonomous vehicles, an understanding of these interactions is needed. Therefore this paper analyzes the interaction between drivers and pedestrians at crosswalks from both the driver's and the pedestrian's perspective. A study with driving experiments in real-life traffic was conducted. The study not only investigates how drivers respond to pedestrians who intend to use the crosswalk, but also their response to pedestrians who waive their right-of-way. An analysis of recorded driving and video data identified typical behavior patterns of drivers approaching a crosswalk, as well as of pedestrians crossing on it. Supplemented by questionnaires filled in by drivers and pedestrians, we determined factors which have a relevant impact on successful driver-pedestrian interaction. We confirmed that with higher initial speeds, drivers tend to exhibit more anticipatory driving behavior. Contrary to previous studies, the presented results show a significant influence of the vehicle's speed on the pedestrian's decision process. The gained insights into the driver-pedestrian interaction process provide an opportunity to develop autonomous driving functions with a cooperative driving behavior that conforms to the expectations of both pedestrians and vehicle occupants.
  @cite_31: Self-driving technologies have been increasingly developed and tested in recent years (e.g., Volvo's and Google's self-driving cars). However, only a limited number of investigations have so far been conducted into communication between self-driving cars and pedestrians. For example, when a pedestrian is about to cross a street, that pedestrian needs to know the intension of the approaching self-driving car. In the present study, we designed a novel interface known as "Eyes on a Car" to address this problem. We added eyes onto a car so as to establish eye contact communication between that car and pedestrians. The car looks at the pedestrian in order to indicate its intention to stop. This novel interface design was evaluated via a virtual reality (VR) simulated environment featuring a street-crossing scenario. The evaluation results show that pedestrians can make the correct street-crossing decision more quickly if the approaching car has the novel interface "eyes" than in the case of normal cars. In addition, the results show that pedestrians feel safer with regard to crossing a street if the approaching car has eyes and if the eyes look at them.
  @cite_34: Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.
  @cite_13: This paper presents a study that aimed to identify the importance of eye contact and gestures between pedestrians and drivers. A video-based observation and coding was undertaken to categorize the road-crossing and communication behavior of pedestrians and drivers in busy traffic situations where efficient negotiation is necessary. The evidence in the study suggests that eye contact does not play a major role in manual driving, that explicit communication is rare to non-existent, and that motion patterns and behaviors of vehicles play a more significant role for pedestrians in efficient traffic negotiations.
summary: Interactions among traffic agents have been explored in prior work by means of explicit communication between pedestrians and drivers @cite_41 , pedestrians and cars @cite_28 @cite_26 @cite_31 , implicit communication @cite_13 , crosswalks scenarios @cite_24 @cite_4 , through the usage of gestures @cite_42 , and by analyzing interactions with objects that may interfere with pedestrian awareness of the environment @cite_22 . Recent work presented by Rasouli and Tsotsos @cite_34 summarized pedestrian behavior studies, discussing interactions between pedestrians and autonomous vehicles, and also presented factors that pedestrians take into consideration when crossing streets. @cite_9 used VR to insert pedestrians in virtual environments. The focus of the study was to estimate the pedestrians' acceptance of autonomous cars. Rothenb " u @cite_28 used a Wizard of Oz approach to analyze the interactions between pedestrians and autonomous cars, where the driver camouflaged themselves to blend in with the seat, simulating the appearance of autonomous vehicles.