Realization of different driving characteristics for autonomous vehicle by using model predictive control

Koga, Ayame; Okuda, Hiroyuki; Tazaki, Yuichi; Suzuki, Tatsuya; Haraguchi, Kentaro; Kang, Zhan

doi:10.1109/ivs.2016.7535467

Cited by 19 publications

(7 citation statements)

References 14 publications

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“…It consists of selecting control command inputs that will lead to desired hardware outputs, using the car's motion model to simulate and optimize outputs over a prediction horizon in the future (Guidolini et al, 2017). Koga et al (2016) use the MPC method for path tracking of a small electrical car. They employ the standard bicycle model to predict car's motion.…”

Section: Path Tracking Methodsmentioning

confidence: 99%

Self-Driving Cars: A Survey

Badué¹,

Guidolini²,

Carneiro³

et al. 2019

Preprint

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We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espírito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

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Section: Path Tracking Methodsmentioning

confidence: 99%

Self-Driving Cars: A Survey

Badué¹,

Guidolini²,

Carneiro³

et al. 2019

Preprint

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“…Investigation of personalized control for AVs has a long history, which began with the tuning of the parameters of the intelligent driver model (IDM) [9]. Later, in [10], [33], the driving styles of autonomous vehicles were modified by changing the parameters of their Model Predictive Control (MPC) systems. However, in these studies, the MPC parameters were manually tuned to suit specific drivers.…”

Section: Personalized Controlmentioning

confidence: 99%

Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers

Bao

Capito

Yang³

et al. 2022

IEEE Access

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“…Therefore, we identify function parameters by observing the driving data to personalize the potential fields. The details of the parameter identification can be found in our previous paper [28]. Then, the vehicle speed, V p,x and V p,y , in the x and y directions, respectively, based on the driver model can be calculated by taking the negative gradient of the potential functions.…”

Section: Potential Field Modelmentioning

confidence: 99%

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

Chen

Yamaguchi

Okuda

et al. 2021

IEEE Trans. Intell. Transport. Syst.

Self Cite

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Advanced driver assistance systems should not only make the driving experience safer and more comfortable, it should also have a positive effect on driving behaviors. In this paper, an instructor-like assistance system for collision avoidance is developed and realized on an actual vehicle. The proposed system is activated only if the driver is not operating the vehicle properly when facing a collision risk. The vehicle control is shared by the driver and the assistance system. It is controlled by servomotors. In order to fulfill this requirement, a constraint satisfaction problem (CSP) is proposed and solved based on safe driving constraints and predictive vehicle states. Vehicle motion is predicted by a combination of a dynamics model and a potential field model that reflects the driver's risk feeling to an obstacle. Improved driving behavior is verified and evaluated quantitatively based on driving simulator data. By comparing the driving data before and after using the assistance system, it is found that distance is increased and speed is reduced when passing an obstacle. As a result, driving behavior becomes safer for collision avoidance due to the system's instruction. Furthermore, an experiment with an actual vehicle also demonstrates the practicability of the control system and shows the influence of different safe driving constraints.

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Realization of different driving characteristics for autonomous vehicle by using model predictive control

Cited by 19 publications

References 14 publications

Self-Driving Cars: A Survey

Self-Driving Cars: A Survey

Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

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