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Abstrak :
Dalam proses berkendara, pengemudi memiliki keterbatasan akan informasi selain dari panel instrumen (dashboard) dan penglihatan mereka, sehingga selalu terdapat resiko bahwa pengemudi lengah dan melakukan kesalahan. Untuk membantu pengemudi, salah satu pengembangan terkini di industri otomotif adalah Driver Assistence System atau DAS, yang ditujukan untuk membantu dengan cara memberikan informasi yang komprehensif mengenai kondisi kendaraan maupun kondisi sekitar kendaraan. Informasi yang didapatkan dapat berupa data kendaraan melalui sensor internal, serta data sensor eksternal seperti Kamera. Sebuah kendala dalam menelaah informasi dari Kamera adalah kemampuan untuk mendeteksi jalan dan mengidentifikasi objek yang ada di sekitar, yang umumnya memerlukan biaya komputasi yang cukup besar, sehingga masih tergolong kurang aksesibel. Dalam penelitian ini, dikembangkan sebuah rancangan sistem gabungan perangkat elektronik dan software, dengan kemampuan membaca data internal kendaraan melalui Sensor Grabber, serta menerima dan menelaah data visual dari Kamera. Algoritma deteksi jalan dan pendeteksian objek dikembangkan menggunakan teknik Image Processing serta Deep Neural Network atau Deep Learning. Data kemudian dapat ditampilkan secara visual melalui Graphical User Interface (GUI) yang dikembangkan dengan bahasa Python. Sistem dilatih dengan sampel berjumlah 816 gambar. Setelah melakukan pengujian, data internal kendaraan dapat diperoleh secara real-time, pendeteksian jalan dapat dilakukan dengan tingkat akurasi sebesar 84.96%, dan objek di sekitar kendaraan dapat diprediksi serta diketahui jarak dan posisinya menggunakan Deep Learning dengan tingkat kepresisian hingga 63.6%, dengan waktu komputasi total 121.68ms. ......During driving, the driver does not have much information regarding the vehicle and its surroundings aside from the instrument panel and their own eyes, therefore there is always the risk of getting caught off-guard and making a mistake. To assist the driver, one of the current breakthroughs in the industry is Driver Assistance System (DAS), which is meant to help drivers by giving them comprehensive information regarding their vehicle or its surroundings. The given information can be the vehicle's data from internal sensors, and data from external sensors such as Cameras. A problem regarding analyzing visual data is how to detect road edges and identify the surrounding objects, which usually requires a sizable amount of computing power, therefore causing the technology to still remain less accessible to the public. In this research, a system consisting of Electronics and software with the ability to retrieve vehicle data via a Sensor Grabber, as well as obtain and analyze visual data via a camera is designed. A Road Edge Detection an Object Detection Algorithm is developed with Image Processing and Deep Neural Network or Deep Learning Techniques. The data is then visualized through a Graphical User Interface (GUI) developed in Python. The system is trained using a sample of 816 images. After a testing process, the internal data of the vehicle can be retrieved in real-rime, road edge detection can be achieved with 84.96% accuracy, and object detection with distance calculation using Deep Learning can be done with 63.6% accuracy, using total computation time of only 121.68ms.

Abstrak :
This fundamental work explains in detail the driver assistance systems for active safety and driver assistance, considering both their structure and their function. These include the well-known standard systems such as Anti-lock braking system (ABS), Electronic Stability Control (ESC) or Adaptive Cruise Control (ACC). But it includes also new systems for protecting collisions protection, for changing the lane, or for convenient parking. The book aims at giving a complete picture focusing on the entire system. First, it describes the components which are necessary for assistance systems, such as sensors, actuators, mechatronic subsystems, and control elements. Then, it explains key features for the user-friendly design of human-machine interfaces between driver and assistance system. Finally, important characteristic features of driver assistance systems for particular vehicles are presented: Systems for commercial vehicles and motorcycles.

Abstrak :
This volume of the Lecture Notes in Mobility series contains papers written by speakers at the 22nd International Forum on Advanced Microsystems for Automotive Applications (AMAA 2018) "Smart Systems for Clean, Safe and Shared Road Vehicles" that was held in Berlin, Germany in September 2018. The authors report about recent breakthroughs in electric and electronic components and systems, driver assistance, vehicle automation and electrification as well as data, clouds and machine learning. Furthermore, innovation aspects and impacts of connected and automated driving are covered. The target audience primarily comprises research experts and practitioners in industry and academia, but the book may also be beneficial for graduate students alike.

Abstrak :
Sebagai salah satu ibukota terpadat di dunia, Jakarta mengalami kenaikan populasi yang cepat setiap tahunnya yang sejalan dengan pertumbuhan jumlah kendaraan bermotor. Masalah muncul ketika Jakarta dinobatkan sebagai salah satu kota yang tidak nyaman dalam hal mengemudi berdasarkan Indeks Kepuasaan Pengemudi yang dirilis oleh Waze (3,37 dari 10) dan sekitar 98 ribu kecelakaan terjadi sepanjang tahun 2017. Advanced Driver Assistance Systems (ADAS) bertujuan untuk meningkatkan performa pengemudi dan keselamatan berkendara. ADAS dapat memberikan peringatan dan melakukan intervensi yang dibutuhkan ketika menghadap situasi tertentu. Dua dari fitur yang diangkat pada penelitian ini adalah Forward Collision Warning (FCW) dan Lane Departure Warning (LDW). Oleh karena itu, tujuan penelitian ini adalah untuk mengukur penerimaan pengemudi terhadap penggunaan sistem dan mengetahui faktor-faktor yang mempengaruhi keinginan pengemudi untuk mengadopsi suatu teknologi. Melalui hasil penelitian didapatkan tiga variabel laten dengan dua belas variabel terukur yang menjadi faktor pengaruh terhadap keinginan pengemudi untuk menggunakan sistem. Rekomendasi untuk meningkatkan penerimaan pengemudi menjadi bagian akhir yang didapatkan berdasarkan evaluasi terhadap variabel yang tidak signifikan.

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As one of the most populous capitals in the world, Jakarta experiences rapid population growth every year which is followed by increasing number of vehicles rapidly too. The problem arise when Jakarta was named as one of the cities not comfortable to drive based on Driver Satisfaction Index 2016 released by Waze (3,37 out of 10) and around 98 thousands accidents occurred in 2017. Advanced Driver Assistance Systems (ADAS) aims to enhance driver performance and improve safety. ADAS can alert and intervene as needed when facing certain situations. Two systems were investigated in this study, Forward Collision Warning and Lane Departure Warning. Therefore, the purpose of this research was to measure driver acceptance using these systems and discovered the factors that affecting behavioral intention to adopt the systems. Through the results of the study found three latent variables with twelves measured variables that are influential factors on the driver`s intention to use the systems. Recommendations for increasing driver acceptance become the final part obtained based on evaluation of variables that are not significant.