Where sensing detects the present, driving policy plans for the future. Human drivers plan ahead by negotiating with other road users mainly using motion cues – the “desires” of giving-way and taking-way are communicated to other vehicles and pedestrians through steering, braking and acceleration. These “negotiations” take place all the time and are fairly complicated – which is one of the main reasons human drivers take many driving lessons and need an extended period of training until we master the art of driving. Moreover, the “norms” of negotiation vary from region to region as the code of driving in Massachusetts, for example, is quite different from that of California, even though the rules are identical.

The challenge behind making a robotic system control a car is that for the foreseeable future the “other” road users are likely to be human-driven, therefore in order not to obstruct traffic, the robotic car should display human negotiation skills but at the same time guarantee functional safety. In other words, we would like the robotic car to drive safely, yet conform to the driving norms of the region.

Mobileye believes that the driving environment is too complex for hand-crafted rule-based decision making. Instead we adopt the use of machine learning to “learn” the decision making process through exposure to data.

Mobileye’s approach to this challenge is to employ what is called reinforcement learning algorithms trained through deep networks. This requires training the vehicle system through increasingly complex simulations by rewarding good behavior and punishing bad behavior. Our proprietary reinforcement learning algorithms add human-like driving skills to the vehicle system, in addition to the super-human sight and reaction times that our sensing and computing platforms provide. It also allows the system to negotiate with other human-driven vehicles in complex situations. Knowing how to do this well is one of the most critical enablers for safe autonomous driving.

For more details on the challenges of using reinforcement learning for driving policy and Mobileye’s approach to the problem, please see: S. Shalev-Shwartz, S. Shammah and A. Shashua. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems: Dec., 2016.