Recommendation of Adaptive Safe Speed Limits for Different Driver Profiles Using Deep Learning

Personalization is a growing topic in the automotive space, where Artificial Intelligence can be used to deliver a customized experience in features like seat positioning and climate control. Considering that the leading cause of accidents is driving at an inappropriate speed, personalizing the speed limit for a driver can greatly improve vehicle safety. Current speed limits apply to all drivers, irrespective of skill, including special speed limits when there are adverse weather conditions. As these speed limits do not consider an individual’s skill and capabilities, the limit could still be inappropriate for a given driver in that specific driving context. Therefore, we propose a system that can profile the driver’s style to recommend a personalized speed limit, based on both the environmental context and their skill in that environment. The system uses a neural network to classify the driver’s behavior in specific environments by monitoring the vehicle data and the environmental conditions. The network is trained to identify situations when the driver is not comfortable in the current driving context and recommends a more appropriate speed limit. This personalized limit is based on traffic sign speed limits, weather conditions, lighting conditions, and the driver profile. Personalization in this feature could reduce the probability of accidents, especially when the driver is safer driving at speeds lower than the road speed limit based on the weather conditions.