Vehicle Connectivity and Automation: A Sibling Relationship

Ha, Paul Young Joun; Chen, Sikai; Du, Runjia; Dong, Jiqian; Li, Yujie; Labi, Samuel

doi:10.3389/fbuil.2020.590036

Cited by 26 publications

(13 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Connectivity could enhance vehicle automation ( Ha et al, 2020 ) and would be an embedded part in the deployed AVs. Connectivity for AVs could include the remote vehicle ignition, doors unlocking, fuel use, and data exchange among vehicles (V2V), to the infrastructure (V2I), to the cloud (V2C) and to everything (V2X).…”

Section: Results Of Own Assessment and From Experts’ Interviewsmentioning

confidence: 99%

How will vehicle automation and electrification affect the automotive maintenance, repair sector?

Grosso

Ioan²,

Krause

et al. 2021

Transportation Research Interdisciplinary Perspectives

View full text Add to dashboard Cite

Highlights Electrification and automation will affect vehicles maintenance and repair (M&R). Drivers influencing M&R demand provides indications on possible future effects. Less moving parts and regenerative braking systems reduce Battery Electric vehicles’ M&R. Hardware and software and rise in VKT influence autonomous vehicles M&R.

show abstract

Section: Results Of Own Assessment and From Experts’ Interviewsmentioning

confidence: 99%

How will vehicle automation and electrification affect the automotive maintenance, repair sector?

Grosso

Ioan²,

Krause

et al. 2021

Transportation Research Interdisciplinary Perspectives

View full text Add to dashboard Cite

show abstract

“…The anticipated benefits include improvements in mobility, travel efficiency, productivity, and safety across various categories of transportation facility users and other stakeholders (FHWA, 2019; Li et al., 2020; Tarko & Anastasopoulos, 2011). Connectivity is considered an inseparable sibling of automation (Ha, Chen, Dong, et al., 2020) and is often discussed within the context of the “Internet of Things,” which enables information sharing between agents in a system. This is consistent with the concept of cooperative awareness within road traffic (i.e., road users and roadside infrastructure are informed about each other's position, dynamics, and attributes (ETSI, 2019).…”

Section: Introductionmentioning

confidence: 99%

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Chen

Dong

et al. 2021

Computer aided Civil Eng

Self Cite

147

View full text Add to dashboard Cite

A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

show abstract

“…More recently, with the emergence of connectivity devices, perception can be further enhanced by vehicle connectivity (this yields the often-termed “connected autonomous vehicle” [CAV]) as more accurate and direct information can be disseminated through the connectivity devices. It has been postulated that the benefits of combined automation and connectivity will exceed the sum of benefits from these two individually (Ha et al , 2020). In the past few years, many advanced learning based approaches have been applied in the CAV operation in contexts including information fusion and cooperative control (Chen et al , 2021; Dong et al , 2021a; Dong, Chen, Joun Ha et al , 2020; Dong et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

“…It has been postulated that the benefits of combined automation and connectivity will exceed the sum of benefits from these two individually (Ha et al , 2020). In the past few years, many advanced learning based approaches have been applied in the CAV operation in contexts including information fusion and cooperative control (Chen et al , 2021; Dong et al , 2021a; Dong, Chen, Joun Ha et al , 2020; Dong et al , 2020). We duly recognize the coupling of connectivity and automation can accentuate the benefits of the latter.…”

Section: Introductionmentioning

confidence: 99%

Development and testing of an image transformer for explainable autonomous driving systems

Dong

Chen

Miralinaghi

et al. 2022

JICV

Self Cite

View full text Add to dashboard Cite

Purpose Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase. Design/methodology/approach This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features. Findings The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction. Originality/value In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

show abstract

Vehicle Connectivity and Automation: A Sibling Relationship

Cited by 26 publications

References 68 publications

How will vehicle automation and electrification affect the automotive maintenance, repair sector?

How will vehicle automation and electrification affect the automotive maintenance, repair sector?

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Development and testing of an image transformer for explainable autonomous driving systems

Contact Info

Product

Resources

About