CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

A merging interaction model explains human drivers' behaviour from input signals to decisions

One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of high-level decisions, safety margins, and low-level control inputs are required to improve the interactive capabilities of automated driving. Existing driver models typically focus on one of these aspects. Unified models capturing all aspects are missing which hinders understanding of the principles that govern human traffic interactions. This in turn limits the ability of automated vehicles to resolve merging interactions. Here, we present a communication-enabled interaction model based on risk perception with the potential to capture merging interactions on all three levels. Our model accurately describes human behaviour in a simplified merging scenario, addressing both individual actions (such as velocity adjustments) and joint actions (such as the order of merging). Contrary to other interaction models, our model does not assume humans are rational and explicitly accounts for communication between drivers. Our results demonstrate that communication and risk-based decision-making explain observed human interactions on multiple levels. This explanation improves our understanding of the underlying mechanisms of human traffic interactions and poses a step towards interaction-aware automated driving

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Vulnerable Users’ Perceptions of Transport Technologies

As the global population continues to grow, age and urbanize, it is vital to provide accessible transport so that neither ageing nor disability constitute barriers to social inclusion. While technology can enhance urban access, there is a need to study the ways by which transport technologies - real-time information, pedestrian navigation, surveillance, and road pricing - could be more effectively adopted by users. The reason for this is that some people, and particularly vulnerable populations, are still likely to reluctantly use (or even avoid using) technologies perceived as 'unknown' and 'complicated'. Based on evidence from British and Swedish case studies on older people's perceptions of the aforementioned transport technologies, as well as on a Swedish case study of visually impaired people's perceptions, this article makes the case that technology is only one tool in a complex socio-technical system, and one which brings challenges. The authors also suggest that although vulnerable populations are not homogeneous when expressing attitudes towards transport technologies, their assessment criteria tend to be 'pro-social' as they usually consider that the societal benefits outweigh the personal benefits. Emphasising aspects linked to the technologies' pro-social potential or relevance to the individual user could increase acceptance

Progress Report 1: Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)

This is the first progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this first phase, the research performed included two main tasks: 1) the development of a sub-model for the representation of the impact of reduced visibility conditions on driving speed and 2) the development of a conceptual model for the study of the impact of the pandemic on shelter availability and destination choice. An experimental dataset collected in a virtual reality environment has been used to develop a sub-model for macroscopic traffic models considering the impact of reduced visibility conditions on driving speed. An application of a calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. Verification testing has been performed as well. A conceptual framework for the development of a destination choice model to be applied in wildfire scenarios has also been developed

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

Relevant Theoretical Characteristics and Trends of the “Field” Era of Live E-commerce: Knowledge Graph Analysis based on Cite Space

Existing research primarily focuses on the two core elements of “people” and “goods”, but research on the “field” element is relatively rare and scattered. Therefore, this paper will analyse the relevant research literature in the core collection database based on the Cite Space software from the two dimensions of field theory and place attachment theory. To systematically review and sort out the theoretical basis of the influence of consumers’ online behavior in live streaming and service scenarios. The current research’s characteristics and status quo are reflected through literature citation data and keyword co-occurrence. The research conclusions: the model application and interpretation situation of the theoretical basis in the live e-commerce “field” era keeps pace with the times; the research on consumer behavior in the live streaming field needs to be further expanded; Few scholars pay attention to the consumer attachment emotion formed by the live streaming scene construction