Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Bao, Peng; Chen, Zonghai; Wang, Jikai; Dai, Deyun; Zhao, Hua-An

doi:10.1109/tits.2023.3300545

“…In order to achieve this, the system must constantly acquire new information about developing traffic conditions while retaining its previous understanding. Furthermore, the system cannot afford to store a significant amount of trajectory data due to its restricted storage resources (Bao et al, 2021). So, in order to perform well on all processed tasks, it is necessary to keep lifelong learning with restricted storage resource.…”

Section: Trajectory Predictionmentioning

confidence: 99%

“…So, in order to perform well on all processed tasks, it is necessary to keep lifelong learning with restricted storage resource. As a consequence, in a bid to achieve lifelong trajectory prediction, a new framework based on conditional generative replay is proposed by the research team from the University of Science and Technology of China (USTC), which handles the problem of catastrophic forgetting due to different types of traffic environments and improve the precision and efficiency of vehicle trajectory prediction (Bao et al, 2021).…”

Section: Trajectory Predictionmentioning

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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“…However, recent research indicates that establishing a unified model and continuously improving its performance through lifelong learning is a more reasonable and promising approach [158]. Unlike traditional learning that adapts to different tasks through offline training, lifelong learning can be incremental, enabling it to acquire new knowledge without the obligation to relearn the already-learned data [159].…”

Section: A Lifelong Learning-oriented Structurementioning

confidence: 99%

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Zhu,

Zhao,

Wang

et al. 2023

Preprint

0

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<p>This review focuses on the integration of intelligent driving and intelligent cockpit systems. With the advancement of intelligent levels, autonomous vehicles are capable of driving in complex driving context. However, the lack of systems that consider both driver and driving scenarios information reduces the success probability of decision-making of autonomous vehicles in driving context where driver and driving scenarios information are tightly coupled during the human-vehicle cooperation stage. To solve this problem, we present the Cockpit-Driving Integration system (CDI) and review perception and decision-making algorithms for CDI systems. Additionally, to achieve human-centric autonomous vehicles, we propose that the CDI system should consider the personalized characteristics of drivers. Finally, we present a framework for CDI systems </p>

show abstract

“…However, recent research indicates that establishing a unified model and continuously improving its performance through lifelong learning is a more reasonable and promising approach [158]. Unlike traditional learning that adapts to different tasks through offline training, lifelong learning can be incremental, enabling it to acquire new knowledge without the obligation to relearn the already-learned data [159].…”

Section: A Lifelong Learning-oriented Structurementioning

confidence: 99%

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Zhu,

Zhao,

Wang

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>This review focuses on the integration of intelligent driving and intelligent cockpit systems. With the advancement of intelligent levels, autonomous vehicles are capable of driving in complex driving context. However, the lack of systems that consider both driver and driving scenarios information reduces the success probability of decision-making of autonomous vehicles in driving context where driver and driving scenarios information are tightly coupled during the human-vehicle cooperation stage. To solve this problem, we present the Cockpit-Driving Integration system (CDI) and review perception and decision-making algorithms for CDI systems. Additionally, to achieve human-centric autonomous vehicles, we propose that the CDI system should consider the personalized characteristics of drivers. Finally, we present a framework for CDI systems </p>

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Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Cited by 6 publications

References 58 publications

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

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

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

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