The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.
Spatial concentrations (or spatial clusters) of moving objects, such as vehicles and humans, is a mobility pattern that is relevant to many applications. Fast detection of this pattern and its evolution, e.g., if the cluster is shrinking or growing, is useful in numerous scenarios, such as detecting the formation of traffic jams or detecting a fast dispersion of people in a music concert. On-Line detection of this pattern is a challenging task because it requires algorithms that are capable of continuously and efficiently processing the high volume of position updates in a timely manner. Currently, the majority of approaches for spatial cluster detection operate in batch mode, where moving objects location updates are recorded during time periods of a certain length and then batch-processed by an external routine, thus delaying the result of the cluster detection until the end of the time period. Further, they extensively use spatial data structures and operators, which can be troublesome to maintain or parallelize in on-line scenarios. To address these issues, in this paper we propose DG2CEP, a parallel algorithm that combines the well-known density-based clustering algorithm DBSCAN with the data stream processing paradigm Complex Event Processing (CEP) to achieve continuous and timely detection of spatial clusters. Our experiments with real-world data streams indicate that DG2CEP is able to detect the formation and dispersion of clusters with small latency while having higher similarity to DBSCAN than batch-based approaches.
This paper describes GrADyS-SIM, a framework for simulating cooperating swarms of UAVs in a joint mission in a hypothetical landscape and communicating through RF radios. The framework was created to aid and verify the communication, coordination, and context-awareness protocols being developed in the GrADyS project. GrADyS-SIM uses the OMNeT++ simulation library and its INET model suite and allows for the addition of modified/customized versions of some simulated components, network configurations, and vehicle coordination to develop new coordination protocols and tested through the framework. The framework simulates UAV movement dictated by a file containing some MAVLink instructions and affected on the fly by different network situations. The UAV swarm’s coordination protocol emerges from individual interactions between UAVs and aims to optimize the collection of sensor data over an area. It also allows simulation of some types of failures to test the protocol’s adaptability. Every node in the simulation is highly configurable, making testing different network topographies, coordination protocols, node hardware configurations, and more a quick task.
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