The automatic operation of the pedals of a motor vehicle represents a first stage of the transition from driving without assistance to rolling in completely autonomous mode. This paper presents the construction of an autonomous control system that actuates the acceleration and braking pedals for a passenger car equipped with an automatic transmission. The system allows the completely autonomous rolling of the vehicle on the chassis dynamometer. This type of control has many advantages such as ensuring a good repeatability of the tests performed or improving the safety in the workplace by reducing human involvement. For this type of automation, a PID controller is used, with the input data being the speed required by the WLTC test cycle. The output data is represented by the position of the actuating elements and the feedback of the system is provided by the actual speed of the motor vehicle. In order to determine the actual speed of the motor vehicle, an incremental optical encoder is mounted to the wheel of the vehicle. The position of the acceleration and braking pedals is determined by the position transducers of the actuating elements. The development of the hardware continues with the design of the assembly that supports the two actuators as well as the parts that allow the connection between the pedals and the execution elements. The actual mechanical stress of the hardware is determined using a finite elements analysis, which requires a 3D model of the automated system components. The stability analysis is performed by checking the tilt of the device and the relative slip between the device and the vehicle floor. For control software design, the Arduino programming environment was chosen, being able to perform the automated control between the input and the output data of the drive system with good accuracy and calculation speed.

Information sharing is essential for autonomous driving supported by communicationsand smart traffic driven by big data. It enables the acquisition of knowledge in cooperative intelligent transport systems (C-ITS), which ultimately increases the integrity and accuracy of road environmental data and thereby improves the efficiency, safety and comfort of the traffic system. Moreover, many cloud services and applications, such as self-healing HD Maps, demand massive information sharing by means of vehicle-to-X (V2X) communications. In this paper, we review two existing frameworks for information sharing in C-ITS, which are open, standardized and commonly accepted: SENSORIS primarily targets at data sharing with and among clouds, specifically for digital maps. V2X-Messaging focuses on road safety and traffic efficiency based on ad hoc communications amongC-ITS-Stations (C-ITS-S), including vehicles, the roadside infrastructure and backends. Then, we analyze both frameworks considering various technical criteria and involved use cases. Finally, we propose a hybrid software architecture that combines both information sharing approaches and facilitates interworking between SENSORIS and V2X-Messaging in order to enhance C-ITS application.

Massive expansion and implementation of Advanced Driver Assistant Systems and advent of Highly Automated Driving functions brings huge challenges in terms of design and development, but also function validation and certification process which is a limiting factor for their market introduction. To ensure safety of such systems, whose complexity is rapidly growing, it is essential to evaluate functionality of automated driving systems within the mandatory certification before it’s deployed on the road. And after their deployment, they must be a subject to periodical technical inspection during life cycle as well. The number of regulations and standards considering safety of AD functions gradually increases, but current safety standards and regulations still have to be adopted and enhanced. For highly automated driving functions and AVs that do not require permanent monitoring by the driver, a theoretically infinite number of possible traffic situations, that a self-driving car could possibly encounter, needs be tested. One promising method to overcome this matter is the scenario-based approach focused on critical, dangerous and extreme situations. Such approach ensures a repeatability and robustness of an approval process if it is supported by a significant sample of harmonized scenarios. Since confronting conventional physical driving tests with this test effort is not feasible anymore, virtualization of testing methods by means of computer simulation needs to be emphasized. To meet above described challenges, TÜV SÜD is developing a methodology for scenario-based evaluation of AD functionality as a supplement for either development or future certification of automated driving systems. The methodology combines virtual-based approach and physical testing and guarantees repeatability of test conditions. Virtual-based testing is provided by an in-house simulation toolchain with an open architecture. The toolchain consists of functional blocks as: database of standardized scenario, virtual environment model, high fidelity physics-based sensor simulation, model of vehicle dynamics, control functions and algorithms, automated and standardized post-processing and reporting. Physical testing provides real-world data measurement used among other purposes for validation of the simulation toolchain and its relevant functional blocks respectively. Physical testing is performed on our own test track using typical equipment as: driving robots, inertial measurement unit, guided soft target, soft VRU targets, master control station and others. In presentation, an overview of the current state of methodology is given and the workflow is demonstrated for a specific operational design domain (ODD). Architecture of simulation toolchain is described and explanation how functional blocks are embedded into overall architecture and how they interact with each other is given. Trustworthiness for virtual test execution will be discussed by means of a comparison and correlation between real-world and virtual-simulation measurement results for a specific operational design domain.