Data-Driven Indoor Positioning Correction for Infrastructure-Enabled Autonomous Driving Systems: A Lifelong Framework

Zhao, Cong; Song, Andi; Zhu, Yifan; Jiang, Shengchuan; Liao, Feixiong; Du, Yuchuan

doi:10.1109/tits.2022.3233563

Cited by 28 publications

(16 citation statements)

References 42 publications

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“…In high-precision positioning at the vehicle end, match and analyze the vehicle end perception results with high-precision base maps to achieve high-precision map change estimation. The cloud focuses on data cleaning and mining based on a large amount of data uploaded from the vehicle and roadside, analyzing data uncertainty to establish a reliable model of map elements [8]. Through high-precision matching and fusion of multi-source data, high-precision reconstruction of map update features is carried out.…”

Section: Basic Architecture For High-precision Map Crowdsource Updatementioning

confidence: 99%

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

Liu

2024

J. Phys.: Conf. Ser.

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In response to the challenges faced by real-time dynamic updates of high-precision maps for autonomous driving in terms of high precision, high reliability, and high safety, this paper summarizes the difficulties and challenges currently faced by high-precision map updates. Accelerate the large-scale commercialization of autonomous driving high-precision maps. Thus, improving the safety and stability of intelligent vehicles and providing critical support for high-level autonomous driving. Firstly, the definition and connotation of autonomous driving maps were described, and the data characteristics and functional applications of autonomous driving maps were pointed out. Secondly, the development status and trends of high-precision map updates were reviewed, and the advantages and disadvantages of centralized map updates and crowdsourced map updates were summarized. Pointing out that crowdsource updates have become a new trend in the development of map updates. Once again, the basic architecture and key core modules of current crowdsource updates were summarized, and an analysis was conducted on key core technologies, summarizing the current status and trends of key technologies involved in crowdsource updates. The results indicate that the current technology still faces 7 main challenges, including map modeling, high-precision positioning, 3D reconstruction, fusion updates, data security, rapid review, and standard laws and regulations to address these challenges.

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Section: Basic Architecture For High-precision Map Crowdsource Updatementioning

confidence: 99%

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

Liu

2024

J. Phys.: Conf. Ser.

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“…And high-precision indoor positioning service is unavoidable in AVP. However, existing wireless indoor positioning technologies, including Wi-Fi, Bluetooth, and ultra-wideband (UWB), have a tendency to degrade significantly with the increase of working time and the change of building environments (Zhao et al, 2023). To handle this problem, a data-driven and map-assisted indoor positioning correction model has been proposed to improve the positioning accuracy for the infrastructure-enabled AVP system recently by a research team from Tongji University, Shanghai, China (for details refer to Ref.…”

Section: Automated Valet Parkingmentioning

confidence: 99%

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

Zhu,

Bu,

Zhu

et al. 2024

Front. Neurorobot.

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The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS’s domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.

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“…In this system, AVs detect road profiles using onboard light detection and ranging (LiDAR), accelerometers, and global positioning systems (GPSs) and then send them to roadside units (RSUs) via vehicle-to-infrastructure communication. Dynamic traffic information can be detected by roadside sensors [13][14][15][16][17]. Furthermore, the multi-source road and traffic information is uploaded to the cloud server for integration.…”

Section: Introductionmentioning

confidence: 99%

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

Chen

Zhao

Jiang

et al. 2023

IJERPH

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Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

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Data-Driven Indoor Positioning Correction for Infrastructure-Enabled Autonomous Driving Systems: A Lifelong Framework

Cited by 28 publications

References 42 publications

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

High precision map crowdsource update technology and SLAM technology - Application in autonomous driving

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

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