Currently, nearly all advanced automotive technologies operate under what are known as deterministic models. In a deterministic system, the outcome is dictated by a set of known initial conditions. These systems offer welcome predictability and repeatability. In contrast, probabilistic models yield different outcomes given the same initial condition, introducing an element of probability guided randomness. The latter model is considered necessary for autonomous vehicle functionality, but this is easier said than done.

Each model type has pros and cons. For starters, in the area of advanced automotive technologies, programming a deterministic model is more manageable. For example, automatic emergency braking systems are generally calibrated to recognize the rear of a preceding vehicle and apply braking when that vehicle slows and a crash is imminent. But how would that same system respond to a boulder rolling onto the roadway in the path of an equipped vehicle? The deterministic model is not well-suited for a curveball like this. The driver wants the same outcome – braking assistance – but the system only recognizes a certain set of predetermined conditions that do not include rolling boulders. 
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