Agreeing to Cross: How Drivers and Pedestrians Communicate

The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of pedestrian behaviors in various street configurations and weather conditions. These examples were selected from approx. 240 hours of driving in the city, suburban and urban roads. The second contribution is an analysis of our data from the point of view of joint attention. We identify what types of non-verbal communication cues road users use at the point of crossing, their responses, and under what circumstances the crossing event takes place. It was found that in more than 90% of the cases pedestrians gaze at the approaching cars prior to crossing in non-signalized crosswalks. The crossing action, however, depends on additional factors such as time to collision (TTC), explicit driver's reaction or structure of the crosswalk.Comment: 6 pages, 6 figure

Predicting pedestrian crossing intentions using contextual information

El entorno urbano es uno de los escenarios m as complejos para un veh culo aut onomo, ya que lo comparte con otros tipos de usuarios conocidos como usuarios vulnerables de la carretera, con los peatones como mayor representante. Estos usuarios se caracterizan por su gran dinamicidad. A pesar del gran n umero de interacciones entre veh culos y peatones, la seguridad de estos ultimos no ha aumentado al mismo ritmo que la de los ocupantes de los veh culos. Por esta raz on, es necesario abordar este problema. Una posible estrategia estar a basada en conseguir que los veh culos anticipen el comportamiento de los peatones para minimizar situaciones de riesgo, especialmente presentes en el momento de cruce. El objetivo de esta tesis doctoral es alcanzar dicha anticipaci on mediante el desarrollo de t ecnicas de predicci on de la acci on de cruce de peatones basadas en aprendizaje profundo. Previo al dise~no e implementaci on de los sistemas de predicci on, se ha desarrollado un sistema de clasi caci on con el objetivo de discernir a los peatones involucrados en la escena vial. El sistema, basado en redes neuronales convolucionales, ha sido entrenado y validado con un conjunto de datos personalizado. Dicho conjunto se ha construido a partir de varios conjuntos existentes y aumentado mediante la inclusi on de im agenes obtenidas de internet. Este paso previo a la anticipaci on permitir a reducir el procesamiento innecesario dentro del sistema de percepci on del veh culo. Tras este paso, se han desarrollado dos sistemas como propuesta para abordar el problema de predicci on. El primer sistema, basado en redes convolucionales y recurrentes, obtiene una predicci on a corto plazo de la acci on de cruce realizada un segundo en el futuro. La informaci on de entrada al modelo est a basada principalmente en imagen, que permite aportar contexto adicional del peat on. Adem as, el uso de otras variables relacionadas con el peat on junto con mejoras en la arquitectura, permiten mejorar considerablemente los resultados en el conjunto de datos JAAD. El segundo sistema se basa en una arquitectura end-to-end basado en la combinaci on de redes neuronales convolucionales tridimensionales y/o el codi cador de la arquitectura Transformer. En este modelo, a diferencia del anterior, la mayor a de las mejoras est an centradas en transformaciones de los datos de entrada. Tras analizar dichas mejoras, una serie de modelos se han evaluado y comparado con otros m etodos utilizando tanto el conjunto de datos JAAD como PIE. Los resultados obtenidos han conseguido liderar el estado del arte, validando la arquitectura propuesta.The urban environment is one of the most complex scenarios for an autonomous vehicle, as it is shared with other types of users known as vulnerable road users, with pedestrians as their principal representative. These users are characterized by their great dynamicity. Despite a large number of interactions between vehicles and pedestrians, the safety of pedestrians has not increased at the same rate as that of vehicle occupants. For this reason, it is necessary to address this problem. One possible strategy would be anticipating pedestrian behavior to minimize risky situations, especially during the crossing. The objective of this doctoral thesis is to achieve such anticipation through the development of crosswalk action prediction techniques based on deep learning. Before the design and implementation of the prediction systems, a classi cation system has been developed to discern the pedestrians involved in the road scene. The system, based on convolutional neural networks, has been trained and validated with a customized dataset. This set has been built from several existing sets and augmented by including images obtained from the Internet. This pre-anticipation step would reduce unnecessary processing within the vehicle perception system. After this step, two systems have been developed as a proposal to solve the prediction problem. The rst system is composed of convolutional and recurrent encoder networks. It obtains a short-term prediction of the crossing action performed one second in the future. The input information to the model is mainly image-based, which provides additional pedestrian context. In addition, the use of pedestrian-related variables and architectural improvements allows better results on the JAAD dataset. The second system is an end-to-end architecture based on the combination of threedimensional convolutional neural networks and/or the Transformer architecture encoder. In this model, most of the proposed and investigated improvements are focused on transformations of the input data. After an extensive set of individual tests, several models have been trained, evaluated, and compared with other methods using both JAAD and PIE datasets. Obtained results are among the best state-of-the-art models, validating the proposed architecture

Pedestrian–vehicle interaction at unsignalized crosswalks: a systematic review

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), to generate a document that supports the development of future research, compiling the various studies focused on the analysis of the pedestrian-vehicle interaction at unsignalized crosswalks. Firstly, 381 studies were identified by applying the search protocol in the database sources; however, only nine studies were included in this review because most of the studies are not focused on this type of crosswalks or have not considered the micro-simulation perspective. For each study, an analysis of the used methodology for data collection was carried out, in addition to what type of model it was applied, including the variables that represent the PVI (Pedestrian-Vehicle Interaction). The outcomes obtained by this systematic review show that although the video camera observation technique is the most used, it is possible to complement them with other tools to add specific field information. Additionally, variables such as the adjacent yields, speed variables vehicles, pedestrian attitude, and the number of pedestrians waiting at the crossing were those most used in the cellular automata model or micro-simulation, which are the commonly developed models to simulate this interaction.This research was funded by “Fundação para a Ciência e a Tecnologia”, through the project AnPeB–Pedestrian behavior analysis based on simulated environments and their incorporation into risk modeling (PTDC/ECMTRA/3568/2014)