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In certain situations, a moving or stationary object can be a barrier for a vehicle. People and vehicles crossing could potentially get hit by a vehicle. Objects around roads as sidewalks, road separator, power poles, and railroad gates are also a potential source of danger when the driver is inattentive in driving the vehicle. A device that can help the driver to brake automatically is known as Automatic Braking System (ABS). ABS is a part of the Advanced Driver Assistance Systems (ADAS), which is a device designed to assist the driver in driving the process. This device was developed to reduce human error that is a major cause of traffic accidents. This paper presents the design of ABS based on fuzzy logic which is simulated in hardware by using a remote control car. The inputs of fuzzy logic are the speed and distance of the object in front of the vehicle, while the output of fuzzy logic is the intensity of braking. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the design of ABS can work according to design.

Gesture recognition technology can be applied in a variety of plans. In this study, a simple hand gesture recognition has been applied to a mobile robot to make it easier for someone to operate. The mobile robot uses differential drive and it is equipped with a Wi-Fi device. The simple hand gesture sensor used in this study is ZX sensor. The sensor outputs are then processed by fuzzy logic control. The use of fuzzy logic in controlling robots makes changes in wheel speed when turning smooth. The response time of the wheel when turning based on testing is around 2-4 seconds with fuzzy logic control and less than 1 second with on-off control.

About 10% of collisions resulting in death is due to frontal collision. Head-on Collision Avoidance System (HCAS) is a device that can prevent a frontal collision by means of braking or evasive. Two ultrasonic sensors are used to monitor the vehicles in front and to the right. Two distances are observed by sensors, then become input for decision making system based on fuzzy logic. This decision-making system output is the vehicle forward, stop, or evasive movement. Simulation result using a remote control car proves that the decision making system designed with fuzzy logic can work with a success rate of over 90%.

Advanced Driver Assistance Systems (ADAS) is a system that has the function of helping the driver to be safe the trip without reducing the convenience. The subsystems developed in ADAS among other Automatic Braking System (ABS) which functions to make braking decisions when there is an object in front of it, Rear-end Collision Avoidance System (RCAS) that serves to avoid rear-end collision, Active Lane Keeping Assist (ALKA) which serves to keep the vehicle from getting off lane from its proper lane, and Adaptive Cruise Control (ACC) which serves to adjust the distance of the car with another car in front. This paper discusses the development of fuzzy logic based algorithms for those subsystems. The developed algorithm test was conducted using experimental car. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the algorithm developed of ABS, RCAS, ALKA, and ACC can work according to design.

Accidents due to cross the lane (lane departure crash) of achieving 19% of all accidents. These accidents mostly caused because the driver is not concentrating or dozing. A device that can help the driver so that the vehicle does not exit lanes known as Active Lane Keeping Assist (ALKA). ALKA is a subsystem of Advanced Driver Assistance System (ADAS) and is now used in several brands of cars namely Audi, BMW, and Ford. ALKA useful to help the driver, especially on roads with monotonous situations such as toll roads. This paper presents the development of fuzzy logic-based algorithm for ALKA. A battery toy car is used as a hardware simulation to test the algorithm in real time situation. The test results at three different speeds are slow, medium, and high indicate that the algorithm can work according to design.

Ride comfort on the highway often interrupted because drivers need to adjust the vehicle speed. Safe distance between vehicles should be maintained is the main reason. The situation of monotonous and high speed will increase the risk of accidents on highway. A device is required by the driver to adjust the vehicle speed during the long distance (cruise) driving on highway without neglecting the safety aspects. The device is known as Adaptive Cruise Control (ACC). The ACC is a subsystem of Advanced Driver Assistance Systems (ADASs) that serves to assist the driver during cruise driving. The working principle of the ACC is the vehicle speed set automatically so that the distance to the vehicle in front remains safe. This paper presents the development of fuzzy logic controller for ACC. The fuzzy inference method used in this study is Mamdani. The result from hardware simulation that using remote control car shows that the fuzzy logic controller can work according to the design. DOI: http://dx.doi.org/10.11591/ijece.v4i6.6734

A device that can help the driver so that the vehicle does not exit lanes known as Lane Keeping Assist System (LKAS). LKAS useful to help the driver, especially on roads with monotonous situations such as toll roads. This paper present the development of fuzzy logic-based algorithm for LKAS. Remote control (RC) car is used as a tool to test the algorithm in real time situation. The test results at three different speeds are slow, medium, and high indicate that the algorithm can work according to design.

Safety and comfort are two important aspects which must be achieved at the time of driving.The level of safety in driving can be improved by reducing driver (human) error. An auxiliary device is required by the driver to avoid an accident. Advanced Driver Assistance Systems (ADASs) are systems to help the driver in the driving process. Overtaking Assistance System (OAS) is the subsystem of ADAS that functions to assist the driver in overtaking process. This paper presents decision-making system based on fuzzy logic for OAS. Inputs of decision-making system are the distance of ego vehicle and a vehicle that will be overtake as well as the distance ego vehicle and the vehicle on the other lane. Decisions of a decision-making system are vehicle doing approaching, tailgating, and overtaking. The result from hardware simulation that using remote control car shows that the decision-making system algorithm can work according to the design.