Abstract: Techniques are disclosed to derive local driving behaviors from naturalistic data, which are then incorporated as guidance into the behavioral layer of an Automated Vehicle (AV) for adaptation to local traffic. Moreover, the local driving behaviors are implemented in the AV performance validation process. The techniques facilitate scaling of this process via automatic crowdsourced behavioral data aggregation from human-driven vehicles, as well as ADAS vehicles and autonomous vehicles, and map information. The disclosed techniques also enable the creation of a spatial-behavioral relational database that provides interfaces for efficiently querying geo-bounded local driving information, enabling customization of automated vehicle driving policies to local norms, and enabling traffic behavior analysis.