Overtaking Assistant System Based on Fuzzy Logic

Basjaruddin, Noor Cholis; Kuspriyanto, Kuspriyanto; Saefudin, Didin; Rakhman, Edi; Ramadlan, Adin Mochammad

doi:10.12928/telkomnika.v13i1.499

Cited by 10 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The assumed behavior of the ego vehicle is predicted according to acceleration candidates. The assumed behavior is expressed as follows: (11) where subscript i is the i-th acceleration candidate.…”

Section: B Prediction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Chae

2020

IEEE Access

View full text Add to dashboard Cite

This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle. INDEX TERMS Autonomous vehicles, autonomous driving, decision-making, motion planning, vehicle control, overtaking, lane change, virtual target.

show abstract

Section: B Prediction Modelmentioning

confidence: 99%

“…A variety of research has attempted to solve the overtaking decision-making problem. For overtaking decision problems, fuzzy logic has been utilized [9]- [11]. Fuzzy logic has the merit of considering various aspects of overtaking.…”

Section: Introductionmentioning

confidence: 99%

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Chae

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Penelitian ini dapat dijadikan dasar untuk pengembangan kendali berbasis rute menggunakan GPS yang memungkinkan kendaraan mampu mengikuti rute yang areanya lebih luas [12]. Penelitian ini juga dapat menjadi dasar dalam pengembangan kendaraan otonom yang saat ini sudah mulai digunakan [13] dan [14].…”

Section: Pendahuluanunclassified

Robot Mobile Otonom Menggunakan Metode Odometry

Rakhman

Basjaruddin

Susanto

2019

tech

Self Cite

View full text Add to dashboard Cite

Robot mobile otonom (autonomous mobile robot, AMR) banyak digunakan baik di industri maupun lingkungan rumah tangga. Robot jenis ini mempunyai kemampuan bergerak menuju tempat tertentu secara otomatis. Penggunaan GPS pada tempat tertutup dan area terbatas belum dapat memenuhi kebutuhan karena keterbatasan resolusi GPS. Pada penelitian ini dikembangkan robot mobile yang mampu menuju titik tertentu dengan memanfaatkan sensor rotary encoder dan metode odometry. Koordinat tujuan diberikan ke robot dan kemudian secara otomatis robot bergerak ke arah titik koordinat tujuan. Pengujian dilakukan dengan memberikan tujuan dengan jarak berbeda yaitu 60 cm, 100 cm, 150 cm, 200 cm, dan 250 cm. Rata-rata galat perhitungan odometry yang dihitung pada jarak tersebut adalah sebesar 0.5 cm, 2.63 cm, 1.65 cm, dan 1.1 cm. Pada pengukuran secara riil didapatkan galat sebesar 7cm, 13cm, 10cm, dan 20 cm. Pengujian rute langsung mempunyai nilai galat yang paling kecil dibanding pengujian rute 1 dan rute 2 yaitu 0,11 cm untuk tujuan x dan 0,25 cm untuk tujuan y. Pada pengukuran posisi riil mempunyai galat x = 3 cm dan y = 2 cm.

show abstract

“…ALC adalah salah satu sub sistem dari Advanced Driver Assistance Systems (ADAS) yaitu perangkat yang dapat membantu pengemudi sehingga perjalanan bisa selamat dan nyaman [8]. Sub sistem ADAS lainnya antara lain Automatic Braking System (ABS) [9], Adaptive Cruise Control (ACC) [10], Lane Keeping Assist System (LKAS) [11] dan [12], dan Overtaking Assistant System (OAS) [13].…”

Section: Pendahuluanunclassified

Adaptive Light Control Berbasis Kendali Fuzzy

Basjaruddin¹,

Saefudin

Fredian

et al. 2018

JRE

Self Cite

View full text Add to dashboard Cite

Abstrak-Lampu mobil standar akan bersinar dengan intensitas tetap meskipun cahaya lingkungan berubah-ubah.Intensitas lampu yang tinggi kadang membuat pengemudi lain silau dan berpotensi menyebabkan kecelakaan. Adaptive Light Control (ALC) adalah sistem pengatur lampu depan mobil yang secara otomatis dapat menyesuaikan intensitas lampu sesuai dengan intensitas cahaya lingkungan. Pada penelitian ini dikembangkan ALC yang mampu mengatur intensitas lampu sesuai dengan intensitas cahaya lingkungan termasuk cahaya lampu mobil di depan. Pengaturan lampu dilakukan menggunakan sistem kendali fuzzy dengan memanfaatkan intensitas cahaya lingkungan dan jarak mobil terhadap objek sebagai masukan. Sistem ALC yang dikembangkan diuji coba pada mobil remote control yang dilengkapi sensor dan mikrokontroler. Hasil pengujian menunjukkan bahwa ALC yang dikembangkan dapat bekerja sesuai rancangan. Kata kunci: Adaptive Light Control, logika fuzzy, lampu depan mobilAbstract-Standard headlights will shine with a fixed intensity even though the environmental light changes. Highintensity lights sometimes make other drivers glare and potentially cause accidents. Adaptive Light Control (ALC) is a car headlights control system that can automatically adjust the intensity of the lamp according to the intensity of the light environment. In this research, we developed ALC which was able to regulate the intensity of the lamp according to the intensity of the environmental light including the light of the car in front. Light settings are performed using a fuzzy control system by utilising environmental light intensity and the distance of the car to the object as input. The developed ALC system was tested on remote control cars equipped with sensors and microcontrollers. The intensity of headlight begins to fade on the distance of the car to the object of 92 cm for objects in front of, 108 cm for objects on the left, and 22 cm for objects on the right.

show abstract

Overtaking Assistant System Based on Fuzzy Logic

Abstract: Abstract

Cited by 10 publications

References 17 publications

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Robot Mobile Otonom Menggunakan Metode Odometry

Adaptive Light Control Berbasis Kendali Fuzzy

Contact Info

Product

Resources

About