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Accident detection systems help reduces fatalities stemming from car accidents by decreasing the response time of emergency responders. Smartphones and their on-board sensors (such as GPS receivers and accelerometers) are promising platforms for constructing such systems. This paper provides three contributions to the study of using smartphone-based accident detection systems.


First, we describe solutions to key issues associated with detecting traffic accidents, such as preventing false positives by utilizing mobile context information and polling on-board sensors to detect large accelerations. Second, we present the architecture of our prototype smartphone-based accident detection system and empirically analyse its ability to resist false positives as well as its capabilities for accident reconstruction. Third, we discuss how smartphone-based accident detection can reduce overall traffic congestion and increase the preparedness of emergency responders.


Emerging trends and challenges

Car accidents are a leading cause of death.  Automated car accident detection can save lives by decreasing the time required for information to reach emergency responders. Conventional vehicular sensor systems for accident detection, such as OnStar, notify emergency responders immediately by utilizing in-vehicle sensors, such as accelerometers and airbag deployment monitors, to detect car accidents. Figure 1.1 shows how traditional accident detection systems operate.

Fig. 1.1: A Traditional Accident Detection System

Sensors attached to the vehicle use a built-in cellular radio to communicate with a monitoring centre that is responsible for dispatching emergency responders in the event of an emergency. Car accident detection and highway congestion control is an emerging application for wireless mobile sensor networks. Recent advances in smartphone technologies are making it possible to detect car accidents in a more portable and cost effective manner than conventional in-vehicle solutions. Rapid accident detection and response can save lives and reduce congestion by alerting motorists as soon as possible, giving them time to reroute. Recent smartphones, such as the HTC Nexus One (an Android-based device), have significantly increased computational abilities compared to previous devices. For example, the Nexus One has a 1 GHz processor and 512MB of RAM compared to the older Palm Treo’s 312 MHz processor and 64MB of RAM. The pervasiveness of smartphones also means that the infrastructure required to establish such a wireless mobile sensor network is already in place and available after installing appropriate application software.

Smartphone manufacturers also have begun including a plethora of sensors that enable devices to detect the context in which they are being used. For example, the HTC Dream (also an Android-based device), possesses a compass, accelerometer, and GPS receiver allowing application developers to determine the geographic position, heading, and movement of the user. The processing power, popularity, and relatively low cost (compared to other traffic monitoring techniques) make smartphones an appealing platform to construct a wireless mobile sensor network that detects car accidents. Smartphone-based accident detection applications provide several advantages relative to conventional in-vehicle accident detection systems, e.g., they are vehicle-independent, increasingly pervasive, and provide rich data for accident analysis, including pictures and videos. Building a smartphone-based wireless mobile sensor network for accident detection system is hard, however, because phones can be dropped (and generate false positives) and the phone is not directly connected to the vehicle. In contrast, conventional in-vehicle accident detection systems rarely incur false positives because they rely on sensors, such as accelerometers and airbag sensors that directly detect damage to the vehicle.


Use on-board sensors and physical context information to detect car accidents

This paper shows how smartphones in a wireless mobile sensor network can capture the streams of data provided by their accelerometers, compasses, and GPS sensors to provide a portable “black box” that detects traffic accidents and records

Data related to accident events, such as the G-forces (accelerations) experienced by the driver. We also present architecture for detecting car accidents based on WreckWatch, which is a mobile client/server application we developed to automatically detect car accidents. Figure 1.2 shows how sensors built into a smartphone detect a major acceleration event indicative of an accident and utilize the built-in 3G data connection to transmit that information to a central server. That server then processes the information and notifies the authorities as well as any emergency contacts.

Fig. 1.2: Smartphone-Based Accident Detection System

WreckWatch   provides functionality similar to an accident/event data recorder by recording the path, speed, and forces of acceleration on a vehicle leading up to and during an accident. It can also notify emergency responders of accidents, aggregate images and video uploaded by bystanders at the scene of an accident, and send pre-recorded text and/or audio messages to emergency contacts. We built WreckWatch using Google Android on the client and Java/MySQL with Jetty and the Spring Framework on the server. The WreckWatch server utilizes custom XML and JSON to communicate with the client applications and the clients use standard HTTP post operations to transmit information to the server. WreckWatch also uses a digital PBX running Asterisk to communicate with first responders and emergency contacts.

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Fig. 1.5: WreckWatch Accident Map


This section compares our work on smartphone-based accident detection systems with related work. Vehicle localization and rapid data acquisition are important to an Intelligent Transportation System (ITS), which utilize sensor networks to monitor traffic conditions and make adjustments to increase safety and reduce congestion on transportation networks. These systems count cars to determine speed and congestion, as well as detect ice build-up and other hazards. An ITS is not limited to highway traffic monitoring and a major advantage to WreckWatch is that it could be utilized in a system designed to monitor trains, buses, or other forms of mass transit.

Using cell phones to construct a wireless mobile network for traffic-related applications is not new. Traffic conditions are often measured via loop detectors that count vehicles and determine their speed. Since these loop detectors are embedded in the pavement there is a high cost associated with their installation and maintenance. Cell phones have been tapped as a potential solution because they provide a substantially larger amount of information at lower installation and maintenance costs.

Loop detectors are often installed in main highways, limiting available information. In addition to lower maintenance costs, cell phone tracking could be available on most roads without installing specialized detection hardware. For instance, the European National Institute for Transport and Safety Research conducted a study that used the volume of cell phones in range of a given tower to identify potential areas of congestion or accidents. This work is similar to WreckWatch in that it utilizes the cellular radios for the communication of information, but the sensors present on the Android device present a much greater level of accuracy and ultimately provide more information than could be gleaned from signal triangulation.



This is Mr.Jose John, 21 yrs old guy, currently pursuing final year mechanical engineering, now become an enthusiastic blogger and a successful entrepreneur.
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