Towards A Framework for Privacy-Preserving Pedestrian Analysis

The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving pedestrian analysis, this paper introduces a framework to anonymize all pedestrians before analyzing their behaviors. The proposed framework leverages recent developments in 3D wireframe reconstruction and digital in-painting to represent pedestrians with quantitative wireframes by removing their images while preserving pose, shape, and background scene context. To evaluate the proposed framework, a generic metric is introduced for each of privacy and utility. Experimental evaluation on widely-used datasets shows that the proposed framework outperforms traditional and state-of-the-art image filtering approaches by generating best privacy utility trade-off

SMPL-Based 3D Pedestrian Pose Prediction

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently prone to suffer from error accumulation along the kinematic chain, which results in unnatural human poses. In joint-rotation-based pose prediction, Skinned Multi-Person Linear (SMPL) parameters are widely used to represent pedestrian pose. In this work, a novel SMPL-based pose prediction network is proposed to address the centrality of each SMPL component by distributing the network weights among them. Furthermore, to constrain the network to generate only plausible human poses, an adversarial training approach is employed. The effectiveness of the proposed network is evaluated using the PedX and BEHAVE datasets. The proposed approach significantly outperforms state-of-the-art methods with improved prediction accuracy and generates plausible human pose predictions

Context-dependent reconfiguration of autonomous vehicles in mixed traffic

Human drivers naturally adapt their behaviour depending on the traffic conditions, such as the current weather and road type. Autonomous vehicles need to do the same, in a way that is both safe and efficient in traffic composed of both conventional and autonomous vehicles. In this paper, we demonstrate the applicability of a reconfigurable vehicle controller agent for autonomous vehicles that adapts the parameters of a used car-following model at runtime, so as to maintain a high degree of traffic quality (efficiency and safety) under different weather conditions.We follow a dynamic software product line approach to model the variability of the car-following model parameters, context changes and traffic quality, and generate specific configurations for each particular context. Under realistic conditions, autonomous vehicles have only a very local knowledge of other vehicles' variables.We investigate a distributed model predictive controller agent for autonomous vehicles to estimate their behavioural parameters at runtime, based on their available knowledge of the system.We show that autonomous vehicles with the proposed reconfigurable controller agent lead to behaviour similar to that achieved by human drivers, depending on the context.Junta de Andalucía MAGIC P12-TIC1814Ministerio de Ciencia, Innovación y Universidades HADAS TIN2015-64841-

EMMON - EMbedded MONitoring

Despite the steady increase in experimental deployments, most of research work on WSNs has focused only on communication protocols and algorithms, with a clear lack of effective, feasible and usable system architectures, integrated in a modular platform able to address both functional and non–functional requirements. In this paper, we outline EMMON [1], a full WSN-based system architecture for large–scale, dense and real–time embedded monitoring [3] applications. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. Then, EM-Set, the EMMON engineering toolset will be presented. EM-Set includes a network deployment planning, worst–case analysis and dimensioning, protocol simulation and automatic remote programming and hardware testing tools. This toolset was crucial for the development of EMMON which was designed to use standard commercially available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. Finally, the EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ nodes testbed

EMMON: a system architecture for large- scale, dense and real-time WSNs

In spite of the significant amount of scientific work in Wireless Sensor Networks (WSNs), there is a clear lack of effective, feasible and usable WSN system architectures that address both functional and non-functional requirements in an integrated fashion. This poster abstract outlines the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON relies on a hierarchical network architecture together with integrated middleware and command&control mechanisms. It has been designed to use standard commercially– available technologies, while maintaining as much flexibility as possible to meet specific applications’ requirements. The EMMON WSN architecture has been validated through extensive simulation and experimental evaluation, including through a 300+ node test-bed, the largest WSN test-bed in Europe to dat

Real-time coordination of autonomous vehicles

Abstract — Autonomous vehicles seem to be a promising approach to both reducing traffic congestion and improving road safety. However, for such vehicles to coexist safely, they will need to coordinate their behaviour to ensure that they do not collide with each other. This coordination will typically be based on (wireless) communication between vehicles and will need to satisfy stringent real-time constraints. However, realtime message delivery cannot be guaranteed in dynamic wireless networks which means that existing coordination models that rely on continuous connectivity cannot be employed. In this paper, we present a novel coordination model for autonomous vehicles that does not require continuous real-time connectivity between participants in order to ensure that system safety constraints are not violated. This coordination model builds on a real-time communication model for wireless networks that provides feedback to entities about the state of communication. The coordination model uses this feedback to ensure that vehicles always satisfy safety constraints, by adapting their behaviour when communication is degraded. We show that this model can be used to coordinate vehicles crossing an unsignalised junction. I