Modelling of Driver and Pedestrian Behaviour – A Historical Review

Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.</p

STUDI TENTANG PEMODELAN ARUS LALU LINTAS

Permasalahan transportasi saat ini masih menjadi pemasalahan utama pada setiap negara, khususnya negara berkembang. Masalah transportasi dihadapkan pada fenomena kemacetan, banyaknya polusi yang dihasilkan oleh kendaraan, sampai kepada masih tingginya tingkat kecelakaan lalu lintas tiap tahunnya. Hal ini, bukan saja disebabkan oleh perilaku pengemudi jalan raya saja, akan tetapi perencanaan arus lalu lintas pun menjadi salah satu faktor yang mempengaruhinya. Salah satu alternatif penyelesaian untuk dapat mengatur dan memanajemen arus lalu lintas adalah dengan memodelkan arus lalu lintas serta mensimulasikannya dalam komputer sehingga dapat diperoleh prediksi-prediksi yang akan terjadi pada simulasi tersebut. Studi literatur mengenai pemodelan dan simulasi arus lalu lintas terus berkembang sejak setengah abad yang lalu dalam upaya memperoleh sebuah pemodelan yang akurat dan mewakili fenomena yang terjadi sebenarnya. Pemodelan arus lalu lintas berbasis komputer dapat dibagi menjadi tiga skala utama, yaitu: mikroskopik, mesoskopik dan makroskopik. Pada skala mikroskopik, pemodelan arus lalu lintas digambarkan sedetail mungkin yang mencakup perilaku setiap kendaraan dan interaksinya. Pada paper ini dilakukan survey terhadap penelitian terdahulu yang membahas mengenai pemodelan arus lalu lintas pada skala mikroskopik. Pada bagian pertama akan dijelaskan gambaran dan pemahaman mengenai pemodelan arus lalu lintas, pemahaman mengenai model mikroskopik arus dan beberapa penelitian mengenai model yang sudah dikembangkan untuk simulasi mikroskopik beberapa tahun terakhir. Selanjutnya, dilakukan pembahasan mengenai pemodelan arus mikroskopik dihubungkan dengan permasalahan transportasi yang ada di Indonesia. Pada paper ini juga memberikan kemungkinan pengembangan penelitian lebih lanjut untuk model mikroskopik lalu lintas

Controlling Longitudinal Safe Distance Between Vehicles

Controlling the safe distances between vehicles on freeways can be used to prevent many accidents. In this research, image-processing techniques have been used to develop an online system that calculates the longitudinal distances between vehicles. This system facilitates controlling safe distances between vehicles without the need for high technology devices. Our approach is real-time and simple, but efficient operations have been used to reduce the image occlusion problem. The main concept of this system is using simple, quick, and effective algorithms for calculating the position of each vehicle in each image. In this way, traffic parameters like speed and distances between vehicles can be calculated for each vehicle in real time. In addition, aggregate parameters like average speed, density, and traffic flow can be calculated using gathered data of single vehicles. As an application of the developed system, controlling the safe distance between vehicles has been introduced. In this system, in case of a driver who does not observe the safe distance, the scene of violation is stored and can be used by the police agencies. KEY WORDS: image processing, traffic, longitudinal safe distance, real time, occlusio

Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems

The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system