Vehicle to infrastructure based safe trajectory planning for Autonomous Intersection Management

Wuthishuwong, Chairit; Traechtler, Ansgar

doi:10.1109/itst.2013.6685541

Cited by 50 publications

(32 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure 2. Schematics for network of autonomous vehicles crossing a lightless traffic intersection and example space-time trajectories that must be separated (Adapted from (Wuthishuwong, 2013)) Logic-based Semantics and Ontologies for Reasoning. To be used in reasoning, data need to be backed by semantics, which provided clear, unambiguous interpretation with respect to the knowledge of the associated domain and the context of use.…”

Section: Semantic Web: Theory Models Languages and Toolsmentioning

confidence: 99%

Semantically‐enabled Model‐based Systems Engineering of Safety‐critical Network of Systems

Petnga

Austin

Blackburn

2017

INCOSE International Symp

View full text Add to dashboard Cite

This paper describes a novel approach to the development and integration of semantics to the model‐based systems engineering and operation of safety‐critical network of systems. Engineering models work directly with formal domain and meta‐domain (especially time and space) knowledge that are determinate, provable (ambiguity free) and executable. Knowledge is encoded as semantic blocks, which are an integration of ontologies, rules, and communication and computation interfaces. These concepts are exercised in a collision avoidance problem involving autonomous agents at a traffic intersection.

show abstract

Section: Semantic Web: Theory Models Languages and Toolsmentioning

confidence: 99%

Semantically‐enabled Model‐based Systems Engineering of Safety‐critical Network of Systems

Petnga

Austin

Blackburn

2017

INCOSE International Symp

View full text Add to dashboard Cite

show abstract

“…In our previous work [32,33], we implemented the AIM for a single intersection. We used V2I with dynamic programming to find an optimal trajectory to safely cross an intersection.…”

Section: Contributionmentioning

confidence: 99%

“…Refer to [32,33], the lower layer is a single autonomous intersection. We use the centralized control principle with V2I communication to manage a vehicle crossing an intersection.…”

Section: Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Consensus-based local information coordination for the networked control of the autonomous intersection management

Wuthishuwong¹,

Traechtler

2016

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

Autonomous intersection management (AIM) will be a future method for improving traffic efficiency in the urban area. Instead of using the traffic signal control like nowadays, it uses wireless communication with autonomous vehicles to support the management of road traffic more safely and efficiently. A single AIM shows an exceptional performance in managing traffics at an intersection. However, it could not be represented a traffic in the real world, which is composed of multiple intersections. We show that coordination of traffic information among vehicles and infrastructures is an essential part of macroscopic traffic management. Coordination of traffic information among the network of AIMs is the key to improve the overall traffic flow throughout the network not only has an optimal flow in some intersections and very heavy traffic in others. In this paper, we introduce the distributed control to a graph-based intersection network to control traffic in a macroscopic level. Vehicle to infrastructure and infrastructure to infrastructure communication are used to exchange the traffic information between a single autonomous vehicle to the network of autonomous intersections. We implement a discrete time consensus algorithm to coordinate the traffic density of an intersection with its neighborhoods and determine the control policy to maximize a traffic throughput of each intersection as well as stabilizing the overall traffic in the network. We use the Greenshields traffic model to define the boundary condition of various traffic flows to the corresponded traffic density and velocity. Our proposed method represents the ability to maintain traffic flow rate of each intersection without having a back up traffic. As well, every intersection operates under the uncongested flow condition. The simulation results of the graph-based networked control of a multiple autonomous intersection showed that the overall traffic flow in the network achieves up to 20% higher than using traffic signal system.

show abstract

“…These algorithms provide smooth car movement, correct route selection [6]. Also these algorithms make it possible to prevent collisions with obstacles [7] and other road users [8], organize safe and rapid intersection crossroad [9--13] and etc. * Corresponding author: wixnin@cit.ifmo.ru These algorithms are designed with two interrelated goals.…”

Section: Introductionmentioning

confidence: 99%

Planning of Autonomous Multi-agent Intersection

et al. 2016

View full text Add to dashboard Cite

Abstract. In this paper, we propose a traffic management system with agents acting on behalf autonomous vehicle at the crossroads. Alternatively to existing solutions based on usage of semiautonomous control systems with the control unit, proposed in this paper algorithm apply the principles of decentralized multi-agent control. Agents during their collaboration generate intersection plan and determinate the optimal order of road intersection for a given criterion based on the exchange of information about them and their environment. The paper contains optimization criteria for possible routes selection and experiments that perform in order to estimate the proposed model. Experiment results show that this model can significantly reduce traffic density compared to the traditional traffic management systems. Moreover, the proposed algorithm efficiency increases with road traffic density. Furthermore, the availability of control unit in the system significantly reduces the negative impact of possible failures and hacker attacks.

show abstract

Vehicle to infrastructure based safe trajectory planning for Autonomous Intersection Management

Cited by 50 publications

References 9 publications

Semantically‐enabled Model‐based Systems Engineering of Safety‐critical Network of Systems

Semantically‐enabled Model‐based Systems Engineering of Safety‐critical Network of Systems

Consensus-based local information coordination for the networked control of the autonomous intersection management

Planning of Autonomous Multi-agent Intersection

Contact Info

Product

Resources

About