Modeling and Simulation of an Autonomous-capable Electrified Vehicle: A Review

Hamilton, Richard; Seager, Hayden; Divakarla, Kavya P.; Emadi, Ali; Razavi, Saiedeh

doi:10.1109/epec.2018.8598294

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, self-driving car technology has still some drawbacks such as reliability and cost. Especially, in real-world applications, the deployment of self-driving car system is quite challenging as expensive hardware components are required (Chen et al, 2018; Hamilton et al, 2018; Van Den Berg and Verhoef, 2016; Wall et al, 2002). Furthermore, there is a need for a legal infrastructure and regulatory changes permitting on-road testing of autonomous vehicles, which is still an ongoing process.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy logic-based autonomous car control system for the JavaScript Racer game

Etlik

Korkmaz

Beke

et al. 2019

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this study, we propose a fuzzy logic-based autonomous car control system and its deployment into the JavaScript Racer game. The main design goal of the self-driving car control system is to end up with an intelligent vehicle control system that is capable to keep the car finish race in the shortest time as possible while keeping the car on the road course regardless the road conditions. To accomplish such a design goal, the proposed intelligent driving system takes up and merges the frameworks of control theory, fuzzy logic and image processing. The proposed structure is composed of vision-based lane detection system, fuzzy logic-based position reference (FPR) generator, fuzzy logic-based velocity reference (FVR) generator, low level position and velocity controllers. The vision-based lane detection system solves the road curve estimation problem in real time and provides this estimation feedback for the FPR and FVR to generate appropriate position and velocity reference signals. The design of the intelligent FPR and FVR generators is accomplished by converting expert knowledge into fuzzy rules. The generated reference signals from the FPR and FVR are then tracked by low level position and velocity controllers. The proposed structure is capable to percept the environment (i.e. road tracking), make decisions (reference signal generation) and control car dynamics (i.e. speed and position) as expected from an autonomous system. The paper presents the structure and design of the intelligent driving system by providing all the design steps in detail. The efficiency of the fuzzy logic-based car control system is shown with results collected from the game environment. It is revealed that the developed intelligent system can successfully operate in various racetracks and different operating points.

show abstract