## 1 Introduction  
Automated Driving Systems (ADSs) offer such conceivable benefits as efficient time management, intensive use of vehicle infrastructure, labour cost savings, congestion reduction, environmental benefits and, above all, a reduced number of accidents, of which 94%(\(\pm\)2.2%) are caused by drivers [1]. Furthermore, the annual consumer and societal benefits of the AVs are estimated to reach $800 billion by 2050 [2]. Nevertheless, further development of automated driving systems relies on the advancements of scientific disciplines and new technologies. While the combination of accumulated knowledge in vehicle dynamics, the emergence of deep learning and the availability of new sensor modalities catalysed AVs research, the challenges of fail-operational architectural models and robust perception are yet to be solved [3]. Initially, the public experienced autonomous driving through the pioneering road-following vehicle Alvin in 1985 developed in the Autonomous Land Vehicle (ALV) project [4]. The simple \"Red minus Blue\" segmentation algorithm achieved autonomy for 4.2 km on a gravel road. Further large scale automated driving studies were carried in Europe's Eurekaproject PROMETHEUS between 1987 and 1995. The project led to the development of VITA II by Daimler-Benz, which succeeded in automatically driving on highways [3, 5]. Since the first automated driving challenge organized by the US Department of Defense in 2004--_Defense Advanced Research Projects Agency's_ (DARPA) Grand Challenge, autonomous driving has become a lasting research topic. Despite famous claims that fully autonomous driving will be available in 2017, the set goal was not achieved and might be delayed for another decade [6, 7]. Notably, the complexity of ADSs operation in indeterminate environments raises the challenge of diverse weather conditions and a need to account for agent behaviour, especially in busy urban environments, where autonomous vehicles have advantages over human drivers [8]. Furthermore, failure to correctly address these challenges has led to accidents that undermine public acceptance of ADSs [3]. In the attempt to reflect lessons learned from various stakeholders, the _Society of Automotive Engineers_ (SAE) defined six levels of driving autonomy where level _zero_ stands for no autonomy and levels _four_ and _five_ stand for autonomy where no human interaction is required [9]. Furthermore, the autonomous systems and their design process must comply with functional safety requirements, i.e., ISO26262 [10]. A solution for addressing safety requirements and achieving high levels of autonomy is the development of perception systems based on multi-modal sensor data fusion using fail-operational architectures. Data fusion enables the collection and association of different sensor data streams to obtain a combined improved representation compared to using the sensors individually. Realizing fail-operational urban surround perception is the joined objective of the authors, and the presented work tackles this challenge at different levels. The work presented in this article represents the European H2020 ECSEL project: Programmable Systems for Intelligence in Automobiles (PRYSTINE). The PRYSTINE project accomplishes Fail-operational Urban Surround perceptION (FUSION) based on reliable camera, RADAR and LIDAR sensor fusion and control functions to enable safe automated driving in urban and rural environments. The intermediate overall results of the project previously have been published in [11, 12, 13]. This article is structured as follows. Section 2 introduces the developed FUSION solutions for high-performance control and intelligence. Section 3 investigates fail-operational architectures--Simplex, 1-out-of-2 with diagnostics (10o2d) and hybrid--and their implications for SAE levels and standardisation. Section 4 describes the EDI DbW car and its perception system. Section 5 demonstrates autonomous passenger vehicle for low-speed autonomy and presents results from an autonomous parking scenario.