A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems

Chung

2021

Sensors

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

Section: Related Workmentioning

confidence: 99%

Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Chung

2021

Sensors

“…HarborNet operates at the Leixões seaport in Porto, Portugal; however, it can be easily tailored to support smart city services and cooperative driving applications. By contrast, the second work [55] presented a prototyping framework as guidance for the development of simulation environments aimed at testing cooperative advanced driver-assistance system (ADAS) applications, namely cooperative emergency lane change (CELC), cooperative adaptive cruise control (CACC), and parking autonomously in cooperative environments (PACE). The proposed approach was based on an iterative prototyping process of application refining and validation, which aimed at identifying a fair compromise between simulating multiple vehicles at the same time and accuracy in modelling their dynamics.…”

Section: Current State Of the Art Of C-its Systemsmentioning

confidence: 99%

An Overview of Cooperative Driving in the European Union: Policies and Practices

et al. 2019

Cooperative-Intelligent Transportation Systems (C-ITSs) aim to connect vehicles, both with one another and with road infrastructures, so as to increase traffic safety and efficiency. This paper focuses on the European framework for supporting the development of Cooperative, Connected, and Automated Mobility, and aims to shed light on the current state of testing and deployment activities in the field at the start of 2019. This may be considered particularly timely given that the year 2019 was identified as the starting date for the deployment of mature services, and the Community legislation is currently paying great attention to the matter. In order to present a concise (but comprehensive) picture, we consulted and analysed the most diverse sources comprising more than 2000 pages.

Journal of Advanced Transportation

“…The vehicles are assumed to have the same powertrain and braking configuration depicted by Figure 2 [32].…”

Section: Vehicle Longitudinal Dynamicsmentioning

confidence: 99%

Distributed H∞ Control of AVs Interacting by Uncertain and Switching Topology in a Platoon

Gao

Dang

et al. 2019

To overcome the challenges arising from the weakness of wireless communication, this paper presents a distributed H∞ control method for multi-AVs connected by an uncertain and switching topology in a platoon. After compensating for the powertrain nonlinearities, we model the node dynamics as a linear uncertain system. By applying the eigenvalue decomposition and linear transformation, the platoon system is decomposed to multiple low order subsystems depending on the eigenvalues of the topological matrix. The sufficient condition ensuring the robust performance of the platoon is presented by using the invariant of signal amplitude of the linear transformation. Then a numerical method is provided to solve the state feedback controller by using the LMI scheme. Only the bounds of the topological eigenvalues are necessary for this new synthesis method and the designed controller can govern the platoon composed of disturbed nodes and interacting by uncertain even switching topologies in a satisfactory way. The effectiveness of this distributed H∞ control strategy is validated by comparative bench tests between nominal and disturbed conditions.