This master thesis is dedicated to the research of neuromorphic perception in the domain of automated driving systems. In view of the rapid progress in the automotive industry and the increasing realization of automated driving, questions regarding the energy efficiency and performance of such systems are coming into focus. This work discusses the possibility of processing lidar data using spiking neural networks to reduce energy consumption in object detection while increasing perceptual capabilities. Automated vehicles not only promise a radical change in the transport sector, but also numerous benefits beyond pure mobility. These include reducing road accidents, reducing congestion, improving access to mobility and saving time in traffic. However, the implementation of automated driving systems faces technical and legal challenges, including the allocation of liability in the event of an accident and the need for intensive research into technical implementation. A key challenge is the perception of the vehicle, which is essential in order to maneuver safely and react to changing traffic conditions. Automated vehicles rely on various sensor systems and often use artificial intelligence methods to recognize objects and perform other tasks for Automated driving. However, these methods require considerable computing power and consume a lot of energy, which is particularly challenging in the context of electromobility, where energy is often limited by battery capacity. This thesis investigates a promising solution to this problem by integrating neuromorphic technology into the perception system. This technology is inspired by the neurobiological information processing of the human brain and aims to develop hardware and software that are similar in function and structure to biological neurons. Because the brain uses discrete impulses to process information, it consumes very little energy compared to conventional computers for comparable tasks. Energy is only consumed when these pulses occur and information is processed. This work focuses on the use of lidar sensors, which emit infrared light and measure the reflected laser beams to determine the distance to objects in the environment. An important parameter is the time of flight of the laser beams. The ToF can be used to convert the lidar data into discrete pulses that the neuromorphic perception system communicates with. The integration of neuromorphic perception systems into automated vehicles has the potential to significantly increase energy efficiency and improve the ability to perceive the environment. In order to analyze the potential and feasibility of a neuromorphic perception system using the ToF of lidar data, a morphological analysis is performed. This analysis takes into account different approaches and aspects that have been elaborated in an extensive literature review. The perception pipeline is divided into subsystems and subfunctions, leading to two concepts that fulfill different requirements for a neuromorphic perception system. These two concepts serve as a starting point for further research and development of this promising technology. The thesis is divided into several chapters that provide a comprehensive overview of the topic of neuromorphic perception systems for automated driving. It covers the basics of automated driving systems, artificial neural networks and spiking neural networks. Lidar sensors, different coding schemes, point cloud object detection, spiking neuron models, SNN hardware implementations and existing approaches are also discussed. The results of the literature review are evaluated based on a morphological analysis. A baseline and an advanced concept of a neuromorphic perception system are presented. In conclusion, this thesis shows the enormous potential of neuromorphic perception systems for automated driving and provides an outlook on possible future developments and research directions. Reducing energy consumption is crucial to achieving the vision of efficient, sustainable and safe mobility. Further research and development in the field of neuromorphic perception technology could be one way to achieve this.