A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.Comment: 2019 IEEE Intelligent Transportation Systems Conference (ITSC

A Methodology for the Design of Safety-Compliant and Secure Communication of Autonomous Vehicles

International audience; The automotive industry is increasing its effort towards scientific and technological innovations regarding autonomous vehicles. The expectation is a reduction of road accidents, which are too often caused by human errors. Moreover, technological solutions, such as connected autonomous vehicle platoons, are expected to help humans in emergency situations. In this context, safety and security issues do not yet have a satisfactory answer. In this paper, we address the domain of secure communication among vehicles - especially the issues related to authentication and authorization of inter-vehicular signals and services carrying safety commands. We propose a novel design methodology, where we take a contract-based approach for specifying safety, and combine it in the design flow with the use of the Arrowhead Framework to support security. Furthermore, we present the results through a demo, which employs model-based design for software implementation and the physical realization on autonomous model cars

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked

QoS Assessment and Modelling of Connected Vehicle Network within Internet of Vehicles

Connected vehicles have huge potential in improving road safety and traffic congestion. The primary aim of this paper is threefold: firstly to present an overview of network models in connected vehicles; secondly to analyze the factors that impact the Quality of Service (QoS) of connected vehicles and thirdly to present initial modelling results on Link QoS. We use the open access Geometry-based Efficient Propagation Model (GEMV2 ) data to carry out Analysis of Variance, Principal Component Analysis and Classical Multi-Dimensional scaling on the link quality for vehicle-2-vehicle (V2V) and vehicle-2-infrastucture (V2i) data and found that both line of sight and non-line of sight has a significant impact on the link quality. We further carried out modelling using system identification method of the connected vehicle network (CVN) in terms of Link QoS based on the parameters identified by the QoS assessment. We evaluated the CVN in terms of a step response achieving steady-state within 80 seconds for V2V data and 500 seconds for V2i data. The work presented here will further help in the development of CVN prediction model and control for V2V and vehicle-2-anything connectivity