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The autonomous car technology promises to replace human drivers with saferdriving systems. But although autonomous cars can become safer than humandrivers this is a long process that is going to be refined over time. Beforethese vehicles are deployed on urban roads a minimum safety level must beassured. Since the autonomous car technology is still under development thereis no standard methodology to evaluate such systems. It is important tocompletely understand the technology that is being developed to designefficient means to evaluate it. In this paper we assume safety-critical systemsreliability as a safety measure. We model an autonomous road vehicle as anintelligent agent and we approach its evaluation from an artificialintelligence perspective. Our focus is the evaluation of perception anddecision making systems and also to propose a systematic method to evaluatetheir integration in the vehicle. We identify critical aspects of the datadependency from the artificial intelligence state of the art models and we alsopropose procedures to reproduce them.

Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment