Autonomous Car-Following Approach Based on Real-time Video Frames Processing

Masmoudi, Mehdi; Ghazzai, Hakim; Frikha, Mounir; Massoud, Yehia

doi:10.1109/icves.2019.8906299

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…They mostly considered data from a 3D-LiDAR (Light Detection and Ranging) mounted on board of the vehicle. To implement Simultaneous Localization and Mapping (SLAM) in AVs, LiDAR point clouds maps are coupled with camera-images and RADAR (Radio Detection and Ranging ) data [14], [15] to assist the control system of the AV to safely navigate through dynamic environments [16]. However, the LiDAR sensors are generally expensive and the resulting computation necessary to interpret, maintain, and fuse data in real-time is most likely going to need powerhungry onboard components such as graphics processing units (GPU) [17].…”

Section: Related Workmentioning

confidence: 99%

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Atik¹,

Brocanelli²,

Grosu³

2022

Preprint

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<p>Future private and public transportation will be dominated by Autonomous Vehicles (AV), which are potentially safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy computational tasks. Providing each AV with powerful enough computing resources is certainly a practical solution but may result in increased AV cost and decreased driving range. An alternative solution being explored in research is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV's reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands. However, this demand shows large spatio-temporal variations. Thus, deploying the same amount of resources in different locations may lead to unnecessary resource over-provisioning.</p> <p>Taking these challenges into consideration, in this paper, we discuss the implications of deploying different amounts of resources in different city areas based on real traffic data to sustain peak versus average demand. Because deploying edge resources to handle the average demand leads to lower deployment costs and better system utilization, we then investigate how peak-hour demand affect the safe travel time of AVs and whether current turn-by-turn navigation apps would still provide the fastest travel route. The insights and findings of this paper will inspire new research that can considerably speed up the deployment of edge-assisted AVs in our society.</p>

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Section: Related Workmentioning

confidence: 99%

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Atik¹,

Brocanelli²,

Grosu³

2022

Preprint

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show abstract

“…The crowd-sourced data may be a potential stream of training data for autonomous vehicles. In conjunction with the data relevant to car-following models [50], computer vision systems can be used to detect other entities on the road. The combination of the data streams would allow vehicles to synthesize different types of data to consider the interactions and therefore a more complex feature space before making decisions, potentially leading to better performance.…”

Section: Data Fusionmentioning

confidence: 99%

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

et al. 2020

Self Cite

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The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions.

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“…Some methods use monitors rather than kinematics sensors to get end-to-end control. Q learning is employed to discrete the action of the vehicle to follow the vehicle [19]. Moreover, deep Q learning is used for agent to take continuous actions for car following [20].…”

Section: Introductionmentioning

confidence: 99%

Modeling Autonomous Vehicles’ Altruistic Behavior to Human-Driven Vehicles in the Car following Events and Impact Analysis

Tang

2023

Journal of Advanced Transportation

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To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed.

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Autonomous Car-Following Approach Based on Real-time Video Frames Processing

Cited by 12 publications

References 9 publications

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

Modeling Autonomous Vehicles’ Altruistic Behavior to Human-Driven Vehicles in the Car following Events and Impact Analysis

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