Street Network Generation with Adjustable Complexity Using k-Means Clustering

Goss, Quentin; Akbaş, Mustafa İlhan; Jaimes, Luis G.; Sanchez-Arias, Reinaldo

doi:10.1109/southeastcon42311.2019.9020392

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Before importing a map to SUMO we pre-process it by using a dicretization approach to correct any inconsistency. We follow the maps' discretization method described by Goss et al [20]. The parameters for map's discretization could be found in Table 1.…”

Section: A Simulation Setupmentioning

confidence: 99%

A Vehicular Crowdsensing Market for AVs

Chakeri

Wang

Jaimes

2022

IEEE Open J. Intell. Transp. Syst.

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The rapid adoption of the vehicles and their on-board sensors as a primary means of transportation make them natural candidates for the outsourcing of data collection. However, vehicles mobility patterns tend to cluster into specific regions such as highways and popular roads, that makes their utilization difficult for data collection in isolated regions with low density traffic. We tackle this problem by proposing a probabilistic incentive mechanism for Vehicular Crowdsensing (VCS) that encourages vehicles to deviate from their pre-planned trajectories in order to visit and collect data from the isolated places. Our proposed framework is able to handle asynchronous vehicles. Also, vehicles consider the traffic holistically to find more profitable routes. By using a realistic vehicular movement data set (UBER movement), open-street maps (OSM) and SUMO vehicular traffic simulator, we show our algorithm significantly outperforms traditional approaches for trajectory generation in terms of spatial and temporal coverage, road utilization, and average participant utility.INDEX TERMS Vehicular crowd-sensing, intelligent mobility, cyber-physical systems, game theory.

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Section: A Simulation Setupmentioning

confidence: 99%

A Vehicular Crowdsensing Market for AVs

Chakeri

Wang

Jaimes

2022

IEEE Open J. Intell. Transp. Syst.

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“…Then, a vertex is placed in the center of each cell, and a directed edge is placed between the corresponding vertices based on the road direction in the actual map that crosses the geographical boundary of one cell and another. A detailed description of this procedure can be found here [30]. Figure 3 shows the difference between the original OSM map and its grid-based version.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing

Chakeri

Wang

Goss

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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In this paper, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs' planned trajectories. We tackle this problem by dynamically modifying the AVs' trajectories and collecting sensing samples from regions otherwise unreachable by originally planned routes. We model this problem as a non-cooperative game in which a set of AVs equipped with sensors are the players and their trajectories are the strategies. Thus, our solution corresponds to a model in which expected individual utility drives the mobility decision of participants. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show our algorithm significantly outperforms traditional approaches for trajectory generation. In particular, our performance evaluation shows a significant lift in crowdsourcer coverage, road utilization, and average participant utility.

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“…SUMO is a popular open-source traffic simulator with a low-overhead for running city-size road networks at a microscopic level (Behrisch et al, 2011). It has been used by both academia and industry in various projects (Goss et al, 2019). CORSIM is also a microscopic traffic simulation system that includes implementations of more complex driving behaviours such as spillback changes and toll plazas (Halati et al, 1997).…”

Section: Related Workmentioning

confidence: 99%

Validation of decision-making in artificial intelligence-based autonomous vehicles

Medrano-Berumen

Akbaş

2020

Journal of Information and Telecommunication

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The autonomous vehicle technology is considered as a significant market disruptor for multiple industries. However, to reach this potential and to be accepted by the public, autonomous vehicles must be proven to be reliable and safe. Therefore, validation is essential for improving the public trust for autonomous vehicles and deploying them for everyday transportation activities. There have been a number of significant efforts on validation of autonomous vehicles; and real-life testing and test tracks have been the major platforms for these activities. However, simulation has also been gaining popularity due to its advantages in cost, time and safety. In this paper, we present a simulation scenario generation methodology with pseudo-random test generation to validate the decision-making system of autonomous vehicles. The methodology separates the validation concerns and focuses on generating scenarios that test the decisions taken by the vehicle. The implementation demonstrates the capabilities and the efficiency of the approach.

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Street Network Generation with Adjustable Complexity Using k-Means Clustering

Cited by 4 publications

References 10 publications

A Vehicular Crowdsensing Market for AVs

A Vehicular Crowdsensing Market for AVs

A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing

Validation of decision-making in artificial intelligence-based autonomous vehicles

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