scholarly journals Lane-keeping control of automated vehicles with feedback delay: Nonlinear analysis and laboratory experiments

2022 ◽  
pp. 104509
Author(s):  
Illés Vörös ◽  
Dénes Takács
Keyword(s):  
Nonlinear Analysis ◽  
Laboratory Experiments ◽  
Feedback Delay ◽  
Automated Vehicles ◽  
Lane Keeping

Lateral Control of Automated Vehicles: On Degraded Mode Control Problems

2001 ◽  
Author(s):  
Shashikanth Suryanarayanan ◽  
Masayoshi Tomizuka
Keyword(s):  
Experimental Testing ◽  
Control Problems ◽  
Phase System ◽  
Minimum Phase ◽  
Automated Vehicles ◽  
Lateral Control ◽  
Management Perspective ◽  
Analysis Design ◽  
Lateral Error ◽  
Lane Keeping

Abstract Lane-keeping operation in automated vehicles used in the PATH* program, is performed with the help of two lateral error measuring sensors mounted on the front and rear bumpers of the vehicles. Prom a fault management perspective, it is natural to investigate the problem of lateral control with just one of these sensors. This paper documents the analysis, design and experimental testing of lateral controllers based on information from either one of these two sensors. In addition, this paper includes a discussion on the problem of lateral control with the help of information from just the rear sensor from a pedagogical perspective. This problem presents an application of control of a non-minimum phase system and leads to counterintuitive results.

scholarly journals Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Stefan Riedmaier ◽  
Daniel Schneider ◽  
Daniel Watzenig ◽  
Frank Diermeyer ◽  
Bernhard Schick
Keyword(s):  
Model Validation ◽  
Safety Assessment ◽  
Measurement Data ◽  
Simulation Models ◽  
Automated Vehicles ◽  
Statistical Validation ◽  
The Real ◽  
Whole Process ◽  
Type Approval ◽  
Lane Keeping

Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests.

scholarly journals A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Na Chen ◽  
Meng Wang ◽  
Tom Alkim ◽  
Bart van Arem
Keyword(s):  
Cost Function ◽  
Traffic Safety ◽  
Control Strategy ◽  
Time Delays ◽  
System Stability ◽  
Feedback Delay ◽  
Automated Vehicles ◽  
Model Uncertainties ◽  
Worst Case ◽  
And Performance

Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.

scholarly journals Bifurcation Analysis of a Lane Keeping Controller With Feedback Delay

2020 ◽  
Author(s):  
Illés Vörös ◽  
Dénes Takács
Keyword(s):  
Initial Conditions ◽  
Numerical Continuation ◽  
Vehicle Speed ◽  
Feedback Delay ◽  
Delayed Feedback Controller ◽  
Parameter Ranges ◽  
Lane Keeping ◽  
Tire Models ◽  
Stable Domains ◽  
Nonlinear Behaviors

Abstract The aim of this study is to highlight nonlinear behaviors and periodic orbits of the single-track vehicle model with a delayed feedback controller. Two widely used tire models, namely a linear tire characteristic and Pacejka’s Magic Formula are considered. Linearly stable domains of parameters such as the vehicle speed and the control gains are determined. Periodic solutions originating from Hopf bifurcation points are followed using numerical continuation and the results obtained with the two different tire models are compared. It is shown that neglecting the saturation of the tire lateral forces at total sliding might change the sense of certain Hopf bifurcations from subcritical to supercritical. The results are verified by numerical simulations. The resulting bifurcation diagrams aim to quantify the degree of robustness of these controllers with regards to the initial conditions at various parameter ranges in order to assure stable and safe operation.

scholarly journals Anticipatory Driving in Automated Vehicles: The Effects of Driving Experience and Distraction

2021 ◽  
pp. 001872082110261
Author(s):  
Dengbo He ◽  
Chelsea A. DeGuzman ◽  
Birsen Donmez
Keyword(s):  
Secondary Task ◽  
Cruise Control ◽  
Automated Vehicles ◽  
Novice Drivers ◽  
Driving Experience ◽  
Driving Behaviors ◽  
Research Gap ◽  
Task Conditions ◽  
Lane Keeping ◽  
Level 2

Objective To understand the influence of driving experience and distraction on drivers’ anticipation of upcoming traffic events in automated vehicles. Background In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

Novel dual‐layer‐oriented strategy for fully automated vehicles’ lane‐keeping system

2020 ◽  
Vol 14 (13) ◽  
pp. 1778-1787
Author(s):  
Quan An ◽  
Shuo Cheng ◽  
Liang Li ◽  
Haonan Peng
Keyword(s):  
Automated Vehicles ◽  
Lane Keeping
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