It will be possible to develop algorithms that can deliver coaching tips and advice that relate to a specific driver in their specific situation” 5 The future of driver development: What AI and machine learning can contribute As trucks become capable of generating larger amounts of data, and developers get better at using this data, it will be possible to develop driver coaching services that are faster, more responsive, and more precise for specific situations. In short, even more intelligent. Artificial intelligence and machine learning are making it possible to cluster larger volumes of data and analyse it for common patterns relating to specific combinations of factors. For example, it could take into different topographies, vehicle configurations, loads, weather conditions, just to name a few. More targeted coaching Currently, connected solutions are based on generic KPIs and do not take into account any external factors that can affect the way someone drives. For example, they can measure vehicle braking but have no wat of knowing if and when braking is needed. However, as systems become better at identifying how specific factors affect driver behaviour, it will be possible to develop algorithms that can take these factors into con- sideration. Coaching tips and advice would then be adapted according to the individual driver and their specific situation. More proactive driver coaching Current connected services for driver coaching tend to be re- active in that they respond to behaviours and events that have already happened. The next step is to develop services that are more predictive and can anticipate what is likely to happen next. For example, by using map-based data, a vehicle can forecast the road ahead and then a connected driver coaching service could potentially offer tips on speed, settings and what features the driver can utilise for even more efficient driving. HOW TECHNOLOGY CONTRIBUTE TO EFFICIENT DRIVING PAGE 6
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