Sensor System Power Adaptation for Automated Vehicles There are two megatrends in the automotive industry for converting the way of traveling in the future. One is the transition from combustion engines to electric vehicles and the second promotes an increased level of automation in future cars. However, automation requires exceptionally high quality environment perception data and various sensor devices, each taking their own share from the vehicle energy storage, which reduces the driving range of electric vehicles. This article proposes an improved ability to save the energy of a vehicle through advanced sensor systems with an adaptive power management of the environment perception system. At low speeds the sensor system is set to a minimal power scheme and at high speed or in adverse weather conditions a high power scheme is used to increase the available sensing range. In addition, it also makes it possible to use V2X communication to restrict sensor power or prohibit the use of certain sensors in radiation sensitive environments.

Network Load Adaptation for Collective Perception in V2X Communications Collective perception uses V2X communications to increase the perception capabilities of vehicles. Relying on the perceived data from their local sensors, nodes exchange information about the objects they detect in their surroundings. An object can be anything significant for the nodesu0027 safety, e.g., obstacles on the road, other vehicles or pedestrians. The amount of data generated by each node is determined by the number of perceived objects and the generation frequency of the messages carrying the detected objects. Considering the limited bandwidth of the wireless channel, the data load generated by collective perception can easily exceed the channel capacity. In this paper, we investigate three schemes that filter the number of objects in the messages and thereby adjust the network load in order to optimize the transmission of perceived objects. Our simulationbased performance evaluation indicates that the use of filtering is an effective approach to improve networkrelated performance metrics, whereas the expected impairment of the perception quality is rather small. The comparison of the filtering algorithms provide insights into the tradeoff between networkrelated metrics and perception quality.