A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Road and Vehicle Condition Monitoring <s> The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Road and Vehicle Condition Monitoring <s> As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver's overall awareness to maximize safety. <s> BIB002
Condition monitoring of either the road or the vehicle is an important aspect of road safety, as deteriorating or damaged roads hold safety risks for travellers, possible vehicle wear and reduced driving comfort for travellers. Monitoring the road surface condition in real-time enables effective and informed road maintenance planning and dispatch. A pattern recognition system on a smartphone was developed in BIB001 that detects road condition from accelerometer and Global Positioning System (GPS) readings. This was accomplished through spectral analysis of tri-axis acceleration signals to retrieve trustworthy road surface anomaly information. The accelerometer data and techniques used in this application are similar to those applied in classifying driving styles, and therefore it is concluded that road surface condition monitoring functionality can also be adopted in the applications of driving style and driver behaviour analysis, through driver-vehicle coupling techniques. Wolverine, a non-intrusive method for traffic and road conditions detection was introduced in  , using accelerometer, GPS and magnetometer sensor data. This application analyse braking events (regular braking can indicate traffic congestions) and bumps in the roads (characterise the type and condition of the road). The work of BIB002 , indicated that the classification of a particular road's overall integrity is not as simple as previously proposed solutions. But despite of this fact, the study proved successful in classifying potholes, bumps and surface type (rough, uneven, and smooth surfaces) using multi-axis accelerometer readings, while the road condition monitoring service provides feedback to the driver regarding unsafe road characteristics in order to improve road safety. The lack of knowledge of a properly functioning vehicle, i.e., a vehicle that is in a good or normal condition, is an area of concern for many drivers. A vehicle that is in a bad condition is a safety risk for the driver of the vehicle and other road users, because of the increased probability of, for instance, engine problems during acceleration and high-speed driving, and slipping of gears due to wear. A method investigated to monitor vehicle condition was presented by the authors of BIB002 , where a mobile smartphone implementation was capable of recognizing gear shifts and irregularities in gear shifting patterns. In this study optimal gear shifting of manual transmission vehicles, for efficient fuel economy, was listed to be in the range of 2300 to 2500 rev/min. It was also mentioned that in the case of automatic transmission vehicles, the recognition of gear slippage can be useful information for early warning of possible gearbox depreciation, low transmission fluid, worn clutch discs, or a faulty shift solenoid. A graphical representation of the gear shifting results, obtained from this implementation, can be seen in Figure 3 . 