This paper surveys the state-of-the-art contributions supporting the validation of virtual testing toolchains for Automated Driving System (ADS) verification. The work builds upon the wellknown limitations of physical testing while conceiving the virtual counterpart as a fundamental ingredient for the type-approval of high automation level ADS. The purpose of the research effort is to summarize computational tools, validation methodologies, and the corresponding fidelity levels delivered by state-ofthe-art simulation toolchains. The ultimate goal is to establish how effectively simulation can play the role of a "virtual proving ground" for ADS certification independently from any specific ADS implementation/effectiveness. The contribution includes classic high-level validation approaches and modern specific computational tools that can be adopted depending on the type of data under analysis. Moreover, the investigation covers approaches embraced both within the scientific community and in technical regulations for the sake of completeness. Ultimately, we identified two high-level validation schema: integrated environment and subsystem-based solutions. In addition, we found that modeling and validating virtual sensors for ADS is the most lacking area from a subsystem-level approach. On the other side, the closedloop interaction between the ADS and other virtual traffic participants makes it difficult to directly compare the experimental results with simulated generated evidence as the emergent behaviors of the ADS may amplify minor discrepancies between the environments.

This paper presents a novel approach to learning predictive motor control via "mental simulations". The method, inspired by learning via mental imagery in natural Cognition, develops in two phases: first, the learning of predictive models based on data recorded in the interaction with the environment; then, at a deferred time, the synthesis of inverse models via offline episodic simulations. Parallelism with human-engineered control-theoretic workflow (mathematical modeling the direct dynamics followed by optimal control inversion) is established. Compared to the latter human-directed synthesis, the mental simulation approach increases autonomy: a robotic agent can learn predictive models and synthesize inverse ones with a large degree of independence. Human modeling is still needed but limited to providing efficient templates for the forward and inverse neural networks and a few other directives. One could consider these templates as the efficient brain network typologies that evolution produced to permit live beings quickly and efficiently learning. The structure of the neural networks-both forward and inverse ones-is made of interpretable "local models", which follows the cerebellar organization (and are also similar to local model approaches known in the literature). We demonstrate the learning of a first-round model (contrasted to Model Predictive Control) for lateral vehicle dynamics. Then, we demonstrate a second learning iteration, where the forward/inverse neural models are significantly improved.

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Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.

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