AbstractOne of the key challenges of anyAutomated Driving(AD) system lies in the perception and representation of the driving environment. Data from a multitude of different information sources such as various vehicle environment sensors, external communication interfaces, and digital maps must be adequately combined to one consistentComprehensive Environment Model(CEM) that acts as a generic abstraction layer for the driving functions. This overview article summarizes and discusses different approaches in this area with a focus on metric representations of static and dynamic driving environments for on-road AD systems. Feature maps, parametric free space maps, interval maps, occupancy grid maps, elevation maps, the stixel world, multi-level surface maps, voxel grids, meshes, and raw sensor data models are presented and compared in this regard.