Risk-informed decision-making and control strategies for autonomous vehicles in emergency situations

Nguyen, Hung Duy; Choi, Mooryong; Han, Kyoungseok

doi:10.1016/j.aap.2023.107305

Cited by 14 publications

(1 citation statement)

References 33 publications

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“…Because of the complex interconnections between the vehicle's lateral and longitudinal dynamics, designing a controller needs to be considered carefully [3] and it continues to remain a challenge. There are various control techniques that have been used for trajectory tracking in automated vehicles, such as: PID [4,5], linear quadratic regulator (LQR) [6][7][8][9], the sliding mode control (SMC) [10,11], robust control [12], model predictive control (MPC) [8,[13][14][15][16] and reinforcement learning [17,18]. However, most of them are used to control longitudinal and lateral dynamics separately.…”

Section: Introductionmentioning

confidence: 99%

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

Bui,

Li,

De Cristofaro

et al. 2023

Applied Sciences

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Lateral vehicle control is of high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The linear quadratic regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self-driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: artificial bee colony optimization (ABC), genetic algorithm (GA), and particle swarm optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as a testing platform, this study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the model-in-the-loop and the hardware-in-the-loop gap up to 78.89% with lateral velocity when we use the relative error criterion (REC) method to validate and 2.35 m with lateral displacement when considering the maximum absolute value method.

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Section: Introductionmentioning

confidence: 99%

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

Bui,

Li,

De Cristofaro

et al. 2023

Applied Sciences

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Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review

Naiseh,

Clark,

Akarsu

et al. 2024

AI & Soc

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Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research.

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Safe Reinforcement Learning-based Driving Policy Design for Autonomous Vehicles on Highways

Nguyen,

Han

2023

Int. J. Control Autom. Syst.

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Risk-informed decision-making and control strategies for autonomous vehicles in emergency situations

Cited by 14 publications

References 33 publications

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review

Safe Reinforcement Learning-based Driving Policy Design for Autonomous Vehicles on Highways

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