CGAN-based System Approach for Mapless Driving on Highways

State-of-the-art L3+ systems for automated driving rely on high-definition (HD) maps in order to cover their safety and performance requirements. HD maps require constant maintenance effort to keep them up-to-date as well as costly solutions for a highly precise global localization of the ego-vehicle. An alternative is to generate a local HD map that depends on the environment sensors only and that does not require a global localization. This contribution presents a lightweight real-time approach for generating local HD maps in highway scenarios. The approach proposes a CGAN in a novel application, namely as a flexible and easily extendable solution for fusing feature maps of divergent quality, content and structure. For proof-of-concept a system integration into an L3+ AD stack was conducted. The gathered online results proof the feasibility of the approach.