Mining Personalized Climate Preferences for Assistant Driving
This addresses the under-researched issue of personalized comfort in assistant driving for travelers, though it appears incremental as it builds on existing IoT and machine learning techniques.
The paper tackles the problem of personalized climate control in vehicles by proposing a novel approach that integrates in-vehicle sensing, driver behavior detection, and preference learning, demonstrating effectiveness with real-world experiments on 11,370 km of driving data.
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.