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Mr. John Smith

Job title



Automated driving brings very high demands on all vehicle systems. In order to meet these requirements, automated vehicles are equipped with various vehicle sensors to collect information about the actual vehicle environment. Current systems are based on data acquired by in-vehicle sensors, such as radar, lidar and camera, which generate a comprehensive environment model where an automated vehicle locates. The sensors differ in their technical performance parameters such as range, resolution, reliability, sensitivity and robustness. The use of heterogeneous sensors allows the technologies to complement each other in terms of their technical properties. The overall safety level is increased by information from several sensors by means of sensor fusion. Assessment errors of the on-board sensors may occur despite the continuous improvement and optimisation of measurements and fusion. Systems under development are especially prone to these errors. Such issues reduce the reliability and trustworthiness of the whole system. These errors, either from sensors or evaluation algorithms and sensor fusion, should be identified during the development of automated driving functions. Virtual driving tests, proving ground tests and, in later development phases, driving tests in real traffic (field tests) serve this purpose. The filed tests in real traffic are crucial for the validation of automated driving systems. Only the real environmental conditions offer a variety of driving situations to prove the safety of automated vehicles. However, the test vehicles in a traffic flow must under no circumstances worsen road safety and put other road users at risk. The project IN2Lab aims to increase the overall test field safety by an infrastructure based safety system installed along a test field. It consists of sensors, C2X communication and mission control centre. This paper presents a concept of Mission Control System. The system provides additional information about traffic flow, obstacles or weather conditions based on data from infrastructure to connected vehicles. Cameras, radars, lidars and C2X roadside units installed along the public test filed to collect data about the traffic flow. The fundamental functionality of the system is the monitoring of traffic flow and object classification. An additional safety value is a quasi-real-time data processing provides relevant information feedback about dynamic and static objects along the test filed to connected vehicles, especially to automated vehicles. These vehicles can use the information to improve their environmental perception confidence or to plan driving manoeuvers.

Mr. Martin Slavík, Technische Hochschule Ingolstadt, GERMANY Prof. Ondřej Vaculín, Technische Hochschule Ingolstadt, GERMANY

Concept of Mission Control System for IN2Lab testing field for Automated Driving

F2021-ACM-119 • Paper + Video • FISITA World Congress 2021 • ACM - Automated and Connected Mobility


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