We describe a computational architecture of a collision early-warning system for vehicles and other principals. Early-warnings allow drivers to make good judgments and to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles, pedestrians, bicyclists, etc.) coexisting in a dense intersection, it is difficult to predict, even a few seconds in advance, since there are many possible scenarios. It is a major challenge to manage computational resources and human attention resources so that only the more plausible collisions are tracked, and of those, only the most critical collisions prompt warnings to drivers. In this paper, we propose a two-stage collision risk assessment process, including the following: 1) a preliminary assessment via simple efficient geometric computations, which thoroughly considers surrounding principals and identifies likely potential accidents, and 2) a specialized assessment that computes more accurate collision probabilities via sophisticated statistical inference. The whole process delivers an expected utility assessment to available user interfaces (UIs), allowing the UIs to make discriminating choices of when to warn drivers or other principals.

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

As sensing technology becomes cheaper and more effective, large networks of intelligent sensors with rich sensing modalities, such as video, will extend into busy large-scale environments in applications such as traffic safety, urban surveillance, and situational awareness. In these unstructured and highly active environments, we consider the problem of detecting, identifying, and monitoring anomalous or suspicious activities among a large number of behaviors of people or vehicles going about their everyday business. Monitoring the activities of all objects in these environments is overly intrusive as well as impractical due to physical limitations of sensing devices and computing power. Therefore, a system to be deployed in such environments must be able to simultaneously (1) ignore objects behaving normally, (2) search for newly emerging anomalous behaviors, and (3) track and monitor objects behaving abnormally. In such systems, the large areas covered and huge amount of data generated require us to implement the system on a distributed platform with limited sensing, processing, and communication resources.Our solution is motivated by the visual attention system in humans, where the eye is attracted to interesting stimuli which pop from the environment, and the brain interprets the most important stimuli in greater detail by focusing the eyes and its mental faculties on them. Our distributed attention architecture consists of a layered information processing module that interprets data from multiple perspectives and a resource allocation module to allocate limited sensing resources based on the system's current knowledge. Thus, distributed attention implements a complete loop, consisting of data observation, information extraction, and finally, sensor control for the next set of observations.The information processing tasks are layered and range from evaluating simple predicates to verifying hypotheses about complex behaviors. A low observation, such as detecting a cluster of pixels moving at an anomalous speed may spawn a vehicle localization and tracking task. This may, in turn, spawn high hypotheses about its behavior or intent, or may evaluate its behavior in relation to other nearby vehicles. Reasoning about these hypotheses may reassure the system that the behavior is normal so that the lower level anomaly detection can be adjusted to ignore similar behavior in the future, or it may lead to further focusing of resources to investigate. By processing data incrementally, the system can use intermediate information to make decisions on what to ignore and on what to focus.Executing a task requires data from some local region of the world. Since there are multiple tasks with different, possibly conflicting data requirements, we have developed a resource allocation module to optimize each task's access to limited sensing resources to maximize the global usefulness of the sensor configuration. In our current implementation, all requests are tagged with a utility value based on the importance of the task they are accomplishing, and sensors are assigned to observe the regions which maximize the sum of the utilities of the all the tasks served. Our testbed to demonstrate this distributed attention system consists of a network of 6 or more OpenBrick x86 Linux boxes, outfitted with steerable pan-tilt cameras which observe a rectangular board (approx. 6' x 8') on the floor where the objects of interest move. The field of view (FOV) of each camera is too small to see the entire board at once. The cameras pan and tilt to discrete directions, so that any one camera can only sense a small portion of the board. Some regions may be observed by multiple cameras, while others only have single coverage. This work is not primarily focused on image understanding, so vehicles and people are represented here as moving, colored dots, performing various behaviors under automatic or manual control. Information processing tasks can jump from node to node, issuing requests to the resource allocator, which points the cameras appropriately.We will demonstrate a scenario with many moving dots flowing normally around the board. One or more objects then exhibit anomalous behavior, like pushing other objects out of the way. The layered information processing module detects anomalous behavior, looking for local regions with statistically unlikely deviations from the learned velocity field, hypothesizing candidate objects that caused the anomalous behavior, tracking them to gather more information, and finally, identifying the behavior based on existing high-level models of anomalous behavior. From the intermediate processing results, the tasks generate data requests for viewing a subset of FOV's, and the resource allocation module then maximizes the utility of the competing tasks by formulating the set of utility tagged requests over possible camera pan-tilt directions as a factor graph and applying the max-sum algorithm (a variant of the sum-product algorithm). The cameras are then moved, new images captured, and the cycle is repeated.

The ability to characterize sensing quality is central to the design and deployment of practical distributed sensor networks. This paper introduces the concept of a sensing field defining, for each point in the physical space of a phenomenon of interest, a measure of how well a sensor network can sense the phenomenon at that point. Using target localization and tracking as examples, the paper derives an upper bound for this measure of goodness measure, using the Cramer-Rao bound and models of sensor observation and network layout. It then evaluates the validity of statistical observation models used by a family of estimators. Simulation results of applying the analytical analysis to a randomly spaced network are presented.

This paper describes information-based approaches to processing and organizing spatially distributed, multimodal sensor data in a sensor network. Energy-constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes information-driven sensor query and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information-based approaches are scalable and make efficient use of scarce sensing and communication resources.