An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

Cloud-Assisted Safety Message Dissemination in VANET-Cellular Heterogeneous Wireless Network

Abstract-In vehicular ad-hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, integrated VANETcellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, while rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies

Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are emerging components of intelligent transport systems (ITS) based on which vehicles can drive in a cooperative way and, hence, significantly improve traffic flow efficiency. However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS). In addition, the downstream traffic information collected by roadside sensors in the V2I communication may introduce measurement errors, which also affect the performance of the CDS. The goal of this paper is to bridge the gap between traffic flow modelling and communication approaches in order to build up better cooperative traffic systems. To this end, we aim to develop an enhanced cooperative microscopic (car-following) traffic model considering V2V and V2I communication (or V2X for short), and investigate how vehicular communications affect the vehicle cooperative driving, especially in traffic disturbance scenarios. For these purposes, we design a novel consensus-based vehicle control algorithm for the CDS, in which not only the local traffic flow stability is guaranteed, but also the shock waves are supposed to be smoothed. The IEEE 802.11p, the defacto vehicular networking standard, is selected as the communication protocols, and the roadside sensors are deployed to collect the average speed in the targeted area as the downstream traffic reference. Specifically, the imperfections of vehicular communication as well as the measured information noise are taken into account. Numerical results show the efficiency of the proposed scheme. This paper attempts to theoretically investigate the relationship between vehicular communications and cooperative driving, which is needed for the future deployment of both connected vehicles and infrastructure (i.e. V2X

Improvement of type declaration of the IEC 61499 basic function block for developing applications of cyber-physical system

The cyber-physical system relies on a network of physical devices. The IEC 61499 standard entails a systematic solution to distributed system development. The basic function block (BFB) is the essential construct of the IEC 61499 architecture. However, the execution semantics of the BFB is not well defined by the standard, which leads to a part of the semantic ambiguity. In this paper, we contribute to improve BFB type declaration by proposing a compact interface model and a strict execution control chart (ECC) model. The improved BFB exhibits less semantic ambiguity, is easier to be created, and is more convenient to be applied in a function block network than the standard BFB

Optimization Method for Crop Growth Characteristics Based on Improved Locality Preserving Projection

Locality preserving projection (LPP) retains only partial information, and category information of samples is not considered, which causes misclassification of feature extraction. An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. Firstly, preliminary dimensionality reduction of sample data is constructed by using twodimensional principal component analysis (2DPCA) to retain the spatial information. Then, two optimized subgraphs are defined to describe the neighborhood relation between different categories of data. Finally, feature parameters set are obtained to extract local information of samples by improved LPP algorithm. The experiments show that the improved LPP algorithm has good adaptability, and the highest SVM classification accuracy rate of this method can reach more than 96%. Compared with other methods, the improved LPP has superior optimized performance in terms of multidimensional data analysis and optimization

Exploring the spatial-temporal relationship between rainfall and traffic flow: a case study of Brisbane, Australia

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather's impact on traffic flow, more specifically, rainfall's impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly