With the advances in wireless communication and mobile computing, a future infrastructureless self-organizing traffic information system, where vehicles can form a network for exchanging traffic information among themselves, will soon be realized. In an infrastructureless traffic information system, vehicles will act as mobile sensors and collect the traffic data as they travel. Smartphones are a great choice for traffic sensing devices as they are now equipped with a variety of sensors such as global positioning system (GPS) receiver, accelerometer, gyroscope, camera, and microphone. These sensors can be exploited to collect traffic data. Although there are many types of sensors available for traffic sensing, past studies have mainly focused on a GPS receiver. However, a GPS receiver consumes a lot of power, and hence it can significantly shorten the battery life. In this paper, we explore a possibility of using other types of sensors on a smartphone for traffic sensing. Particularly, we investigate whether it is possible and how accurate it is to estimate the average speed of a vehicle from the data sensed by an accelerometer. Two estimation methods will be introduced and their accuracy will be evaluated.
Lane change is a valuable piece of traffic information. An abnormally high number of lane changes on a road section typically suggests that some lanes are blocked due to traffic incidents. Currently, the lane change information of vehicles on an urban road is typically obtained from over-roadway fixed sensors such as surveillance cameras. However, using fixed sensors has limitations in terms of cost and coverage. It is more effective to collect the lane change information directly from each individual vehicle. In addition, lane changing behavior of a driver can help assess his driving risk. One convenient way to detect a lane change directly from each vehicle is to take advantage of sensors on smart mobile devices such as smartphones. In this paper, we explore a new way to identify a lane change event based on a pattern of steering wheel angles detected by a smartphone. This is distinguishable from all of the existing steering wheel-based lane change detection methods, which need to retrieve the steering wheel angle signal from the On-board Diagnostic (OBD) port of the vehicle via the Controller Area Network-Bus (CAN-Bus). In addition, unlike others, our method does not require a lot of complex features to make an accurate detection. In fact, we demonstrate that a high level of accuracy can already be attained with a single simple feature called rotation span. Results show that the proposed detection method performs very well both in terms of precision and recall. INDEX TERMS Lane change detection, hidden Markov model (HMM), mobile sensing.
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