Event-Triggered Robust Path Tracking Control Considering Roll Stability Under Network-Induced Delays for Autonomous Vehicles

Viadero-Monasterio, Fernando; Nguyen, Anh-Tu; Lauber, Jimmy; Boada, Maria Jesus L.; Boada, Beatriz L.

doi:10.1109/tits.2023.3321415

Cited by 29 publications

(13 citation statements)

References 53 publications

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“…Figure 1 depicts that the co-driving process can be conceptualized into three phases: situational awareness, decision-making, and control execution. Regarding the method of driving control, Fernand et al [9] proposed a Multiple Input Multiple Output (MIMO) approach for path tracking control of autonomous vehicles under network-induced delays, aimed at enhancing driving safety and passenger comfort. The risk of distracted driving increases when the driver's perception is not fully integrated into the feedback loop.…”

Section: Co-driving and Driving Distractionmentioning

confidence: 99%

Evaluating Driver Preferences for In-Vehicle Displays during Distracted Driving Using Driving Simulators

Li,

Yang,

Zeng

et al. 2024

Electronics

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Research on the impact of distracted driving on in-vehicle displays was conducted through a user preference experiment based on simulated driving. The development of automotive automation and multi-screen in-vehicle displays has improved the driving experience but also increased distracted driving. Typical scenarios and information needs were identified through user research, and a simulated driving platform was used to record user choices of different display modes and their reaction times after being distracted. Questionnaires were analyzed using the Analytic Hierarchy Process (AHP). Results showed that in the manual driving group, users preferred Head-Up Display (HUD), which had the highest efficiency in terms of reaction time. The study confirmed that as automation levels increase, distraction scenarios will change, and user focus will shift from safety to experience.

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Section: Co-driving and Driving Distractionmentioning

confidence: 99%

Evaluating Driver Preferences for In-Vehicle Displays during Distracted Driving Using Driving Simulators

Li,

Yang,

Zeng

et al. 2024

Electronics

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“…Common methods include Linear Quadratic Regulator (LQR) control [ 36 ], sliding mode control [ 37 ], robust control [ 38 ], and model predictive control [ 39 , 40 ]. One study [ 41 ], considering the roll dynamics and network-induced delays, proposed a new multi-input, multi-output linear parameter-varying controller for path-tracking control. Another study [ 42 ] proposed a strategy based on the path-tracking preview algorithm and the LQR controller to improve the lateral stability of the vehicle and address the crosswind issue during driving.…”

Section: System Integration and Validation Test Of Dalkamentioning

confidence: 99%

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Chen,

Lan

et al. 2024

Sensors

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To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.

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“…It is also crucial to mention that vehicle rollover and sideslip impact driving behaviour and, therefore, car instability, contributing to a significant number of fatal accidents [40,41]. Fernando et al [42] developed an IoT (Internet of Things) system to estimate the precise vehicle rollover and sideslip angle by taking into account the communication delay, with the aim of improving driving comfort and safety.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Controller for Drivers’ Steering-Wheel Operating Behaviour in Haptic Assistive Driving System

Noubissie Tientcheu,

Du,

Djouani

et al. 2024

Electronics

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An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance.

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Event-Triggered Robust Path Tracking Control Considering Roll Stability Under Network-Induced Delays for Autonomous Vehicles

Abstract: This paper proposes a multi-input multi-output (MIMO) method for path tracking control of autonomous vehicles under network-induced delays while taking into account the roll dynamics to improve both the driving safety and the passenger comfort. The steering control is directly applied to

Cited by 29 publications

References 53 publications

Evaluating Driver Preferences for In-Vehicle Displays during Distracted Driving Using Driving Simulators

Evaluating Driver Preferences for In-Vehicle Displays during Distracted Driving Using Driving Simulators

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Data-Driven Controller for Drivers’ Steering-Wheel Operating Behaviour in Haptic Assistive Driving System

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