Towards Risk Estimation in Automated Vehicles Using Fuzzy Logic

Gonzalez, Leonardo; Martí, Enrique; Calvo, Isidro; Ruíz, Alejandra; Pérez, Joshue

doi:10.1007/978-3-319-99229-7_24

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The diagram of this simplified vehicle 'bicycle model' is shown in Figure 4. Then, combining equations (16)- (18) with equations (23) and (24), the vehicle dynamic model could be described by the following state equation…”

Section: Lower Controller Of Linear Time-variant-mpcmentioning

confidence: 99%

“…An algorithm of collision risk assessment is proposed via lane-based vehicle motion prediction, 21 but the index of risk is merely the time to collision. The Bayesian networks 22 and fuzzy logic 23 are also used to assess the collision risk for autonomous driving. Fahmy et al developed a risk assessment algorithm to help the vehicle keep the current lane and avoid the obstacle; however, the authors only consider the obstacle as a part of the road geometry.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Chen

Pei

Sun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

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Section: Lower Controller Of Linear Time-variant-mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Chen

Pei

Sun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…However, PID controllers cannot handle the system constraints, and they cannot guarantee efective performance in diferent maneuvers. In [8], a car-following structure is developed for AVs based on fuzzy logic. Te introduced control mechanism is developed for the AVs on the highways to provide a safe trafc stream.…”

Section: Introductionmentioning

confidence: 99%

A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles

2023

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Autonomous driving has recently been in considerable progress, and many algorithms have been suggested to control the motions of driverless cars. The model predictive controller (MPC) is one of the efficient approaches by which the speed and direction of the near future of an automobile could be predicted and controlled. Even though the MPC is of enormous benefit, the performance (minimum tracking error) of such a controller strictly depends on the appropriate tuning of its parameters. This paper applies the particle swarm optimization (PSO) algorithm to find the global minimum tracking error by tuning the controller’s parameters and ultimately calculating the front steering angle and directed motor force to the wheels of an autonomous vehicle (AV). This article consists of acquiring vehicle dynamics, extended model predictive control, and optimization paradigm. The proposed approach is compared with previous research in the literature and simulation results show higher performance, and also it is less computationally expensive. The simulation results show that the proposed method with only three adjustable parameters has an overshoot of about 8% and its RMSE is 0.72.

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“…adding (retrospective) temporal aspects to the measurement. Most criticality metrics only quantify over a subset of the influencing factors that are associated with criticality, such as spatial, temporal, dynamical, perceptual, or environmental circumstances [29].…”

Section: Introductionmentioning

confidence: 99%

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Westhofen

Neurohr

Koopmann

et al. 2022

Arch Computat Methods Eng

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The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today’s society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application’s requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle’s safety performance are a key requirement for demonstrating its trustworthiness.

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Towards Risk Estimation in Automated Vehicles Using Fuzzy Logic

Cited by 9 publications

References 11 publications

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

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