  Both assistant driving and self-driving have attracted a great amount of
attention in the last few years. However, the majority of research efforts
focus on safe driving; few research has been conducted on in-vehicle climate
control, or assistant driving based on travellers' personal habits or
preferences. In this paper, we propose a novel approach for climate control,
driver behavior recognition and driving recommendation for better fitting
drivers' preferences in their daily driving. The algorithm consists three
components: (1) A in-vehicle sensing and context feature enriching compnent
with a Internet of Things (IoT) platform for collecting related environment,
vehicle-running, and traffic parameters that affect drivers' behaviors. (2) A
non-intrusive intelligent driver behaviour and vehicle status detection
component, which can automatically label vehicle's status (open windows, turn
on air condition, etc.), based on results of applying further feature
extraction and machine learning algorithms. (3) A personalized driver habits
learning and preference recommendation component for more healthy and
comfortable experiences. A prototype using a client-server architecture with an
iOS app and an air-quality monitoring sensor has been developed for collecting
heterogeneous data and testing our algorithms. Real-world experiments on
driving data of 11,370 km (320 hours) by different drivers in multiple cities
worldwide have been conducted, which demonstrate the effective and accuracy of
our approach.
