Hierarchical Lateral Control Scheme for Autonomous Vehicle with Uneven Time Delays Induced by Vision Sensors

Qi, Liu; Liu, Yahui; Liu, Congzhi; Chen, Baiming; Zhang, Wenhao; Li, Liang; Ji, Xuewu

doi:10.3390/s18082544

Cited by 22 publications

(14 citation statements)

References 43 publications

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“…However, safety risks can arise from control algorithms' potential inaccuracies in modelling the AV's motion, particularly amid unexpected road conditions. Geometric and kinematic control algorithms are recognised for their simplicity and relatively low computational cost [139], but as they only model the vehicle's geometrical dimensions and kinematic properties such as acceleration and velocity [138], they can lead to inaccuracies and vehicle instability due to their neglect of vehicle dynamics. Without considering vehicle dynamics such as friction forces, tire slips and energy, geometric and kinematic control algorithms can lead to risky driving behaviour at high speeds where dynamics significantly influence the vehicle's motion, such as during sudden lane changes or attempts to avoid unexpected obstacles [138,140].…”

Section: Controlmentioning

confidence: 99%

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

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Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.Sustainability 2019, 11, 5791 2 of 28 surroundings and algorithms that process the data, make, and execute driving decisions through the vehicle's actuators. The quality of data retrieved by the AV's sensors is critical for decision-making [21], and the efficiency, precision and reliability of decision-making algorithms allows AVs to surpass the typical human driver in performing driving tasks [22]. A major component in AVs is machine-learning (ML) [23] algorithms that continuously learn and adapt to new information, which is essential for responding to unexpected situations and providing on-demand transportation services [24]. Algorithms' independence from human input and ML's data-driven nature allows AVs to significantly reduce or eliminate human errors that have been responsible for 90% of road fatalities, such as speeding, alcohol impairment, distractions and induced fear [25,26].However, an overemphasis on technological solutions alone for economic development could risk neglecting social and environmental considerations and thus hinder true "smartness" [27]. Scholars have cautioned against rushing to develop smart mobility solutions such as AVs without being prepared to manage their potential "negative externalities" [1,28]. In particular, issues in algorithmic decision-making in AVs can have undesirable effects on safety and equity. Firstly, data mining processes in AVs are susceptible to biases that lead algorithms to prioritise the safety of certain groups of road users over others and thus, perpetuate discrimination [29,30]. Secondly, many scholars stress the need to design algorithms with ethical considerations to ensure that AVs make ethical driving dec...

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Section: Controlmentioning

confidence: 99%

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

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“…33 A hierarchical lateral control scheme is proposed, where the upper controller is designed by the linear quadratic regulator algorithm based on robust H-infinity to compensate the sensor-induced delays. 34 In view of parametric uncertainties, external disturbances, and over-actuated features, an adaptive hierarchical control framework is proposed which applies an adaptive sliding mode high-level control law to produce a vector of front steering angle and external yaw moment. 35,36 The learning algorithm is also applied to the motion control gradually.…”

Section: Introductionmentioning

confidence: 99%

Adaptive trajectory tracking control strategy of intelligent vehicle

Zhang

Zhao

Zhu

et al. 2020

International Journal of Distributed Sensor Networks

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The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

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“…Algorithms related to perception and localization may also induce significant amounts of delay, especially if visionbased solutions are used (Pendleton et al (2017)). Although better hardware and more efficient algorithms are introduced day by day, most commercial solutions are still not capable of processing the large amounts of sensor data at a high frequency and it is difficult to find a balance between accuracy, processing time and hardware costs (Liu et al (2018(Liu et al ( , 2017).…”

Section: Introductionmentioning

confidence: 99%

The effects of tire dynamics on the performance of finite spectrum assignment of vehicle motion control

Vörös

Várszegi

Takács

2020

Journal of Vibration and Control

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The lateral position control of the vehicle is analyzed in the presence of time delay. To compensate the negative effects of dead time, the predictor control approach called finite spectrum assignment is applied. This controller includes a linear model of the plant and uses the solution of this model over the delay interval to predict the current system states. The focus of the article is whether to include tire dynamics in the predictive model of the controller. Although the more detailed model should improve control performance, the additional parameters (e.g., tire stiffnesses and yaw moment of inertia) are difficult to determine accurately. The effects of parameter mismatches are analyzed in detail, and recommendations are given to ensure safe control of the vehicle. It is shown that the inclusion of tire dynamics in the predictive model vastly improves control performance even in the presence of large parameter errors, but in certain cases, the inaccuracies may lead to instability.

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Hierarchical Lateral Control Scheme for Autonomous Vehicle with Uneven Time Delays Induced by Vision Sensors

Cited by 22 publications

References 43 publications

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Adaptive trajectory tracking control strategy of intelligent vehicle

The effects of tire dynamics on the performance of finite spectrum assignment of vehicle motion control

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