PRIDE (Prediction In Dynamic Environments) is a hierarchical multi-resolution framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS reference model architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. This framework supports the prediction of the future location of moving objects at various levels of resolution, thus providing prediction information at the frequency and level of abstraction necessary for planners at different levels within the hierarchy. To date, two prediction algorithms have been applied to this framework, the long-term and the short-term prediction algorithms.
The PRIDE framework
PRIDE is a hierarchical multi-resolution framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS reference model architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. This framework supports the prediction of the future location of moving objects at various levels of resolution, thus providing prediction information at the frequency and level of abstraction necessary for planners at different levels within the hierarchy. To date, two prediction algorithms have been applied to this framework, the long-term and the short-term prediction algorithms.
The Prediction Algorithms
|Figure 1. Process flow of the LT prediction algorithm.|
At the lower levels, PRIDE utilizes estimation theoretic short-term predictions via an extended Kalman filter-based algorithm to predict the future location of moving objects with an associated confidence measure. The Extended Kalman Filter (EKF) is a well-established recursive state estimation technique where estimates of the states of a nonlinear system are obtained by linearization of the nonlinear state and observation equations. Within the PRIDE framework, the EKF predicts short-term estimate of objects moving at variable speeds and at given look-ahead time instants (every one tenth of a second). It should be noted here that, in contrast to the long-term predictions, the estimation-theoretic short-term prediction algorithm does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory.
To compute the future position of an autonomous vehicle, PRIDE must have knowledge of the environment where the vehicles evolve. For an autonomous vehicle to be able to navigate a road network, it must be aware of and must respond appropriately to any object it encounters. This includes for example, other vehicles, pedestrians, debris, construction, accidents, emergency vehicles, and it also includes the roadway itself. The road network must be described in such a way that an autonomous vehicle knows, with great precision and accuracy, where the road lies, rules dictating the traversal of intersections, lane markings, road barriers, road surface characteristics, and other relevant information.
All this information is essential for an autonomous vehicle to be able to navigate a road network and can be found in the road network database (RND), developed at the National Institute of Standards and Technology (NIST) as part of the Defense Advanced Research Project Agency (DARPA) Mobile Autonomous Robotics Systems (MARS) Program.
In PRIDE, an autonomous vehicle is able to get all information from the RND, based on its position in the road network. The PRIDE algorithms consider different information from the road network structure. For instance, the length and the width of a lane are used to compute the future location of the autonomous vehicle within this lane, the curvature center is used so that the vehicle takes the right angle to turn, the autonomous vehicle has to know the speed limit on a particular lane to avoid speed excess, etc. Some components of the RND used by PRIDE are depicted in Figures 2 and 3 and described below.
|Figure 2. Lane segments (shaded parts).||Figure 3. A lane (shaded part).|
Different drivers drive in different ways. One driver may be very conservative, only changing lanes when absolutely necessary, never exceeding the speed limit, etc. On the other hand, another driver may drive very aggressively, weaving in and out of lanes, greatly exceeding the speed limit, and tailgating other drivers. In most cases, one would experience both kinds of drivers on any trip (along with many drivers that fall somewhere in the middle), and a moving object prediction framework needs a mechanism to account for all such circumstances. When a driver is first encountered, it is extremely rare that one can instantaneously determine the perceived aggressivity of the driver. This information is often determined after observing the driver for a certain amount of time, characterizing their driving behaviors, and assigning aggressivity. The aggressivity that is assigned greatly impacts PRIDE’s predictions as to where that driver will be at times in the future. For example, we would likely assume that a conservative driver would remain in their lanes whenever possible and stay a safe distance behind the vehicle in front of it. An aggressive driver would have a higher probability of changing lanes. We may also find that the aggressivity of the driver may change over times. There are times when one can observe a driver for many seconds at a time. In this case, the driver’s aggressivity may change, perhaps they are very aggressively trying to get to a certain lane but become more passive when they get there. The PRIDE framework addresses all of these driver types and all of the situations mentioned above.
In recent years there has been a growing appreciation of the importance of situation awareness (SAw) in a variety of complex task domains, such as aviation, nuclear power plant management, medical decision-making and tactical driving. Since its original conception, numerous definitions of SAw have been proposed. PRIDE uses the formal definition from Endsley where SAw is described as: [An expert’s] perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future.
To make assumptions of the future positions of moving objects, PRIDE has access to a level of SAw of how other vehicles in the environment are expected to behave considering their own situation. An autonomous vehicle should be able to plan a path while avoiding any collision with obstacles or other moving objects on the road. The autonomous vehicle also requires knowledge of the environment and knowledge on the status of other objects in the environment to be able to drive tactically. Modeling the behaviors of other vehicles is the most important aspect of tactical driving. It is straightforward to model the speed and the relative positions of the vehicles; however, it is a complicated task to model the future behavior of the drivers.
The model of SAw simulated by PRIDE is based on the model provided by Endsley, as sketched in Figure 4.
|Figure 4. Endley’s model for situation awareness|
Large-scale coordination tasks in hazardous, uncertain, and time stressed environments are becoming increasingly important for fire, rescue, and military operations. Substituting robots for people in the most dangerous activities could greatly reduce the risk to human life. Because such emergencies are relatively rare, there is little opportunity to insert and experiment with robots. USARSim was designed as a high-fidelity simulation of Urban Search And Rescue (USAR) robots and environments intended as a research tool for the study of Human-Robot Interaction (HRI) and multi robots coordination. USARSim is an open source simulation environment which provides realistic environments. The environments are full 3D worlds that have photo-realistic textures and objects (Figure 5).
|Figure 5. 3D worlds in USARSim|
The communication between the PRIDE framework and the controller (MOAST) is possible through NML channels at the PRIM level as depicted in Figure 6. The data transfers between these two modules can be summarized as follows: 1) PRIDE queries the NML channels on the status of the autonomous vehicles in the environment (e.g., the number of vehicles) and the characteristics for each vehicle (e.g., orientation, speed). 2) Using this information, the LT algorithm queries the RND on the road structure to identify the position of each autonomous vehicle in the road network. 3) Once the road structure for an autonomous vehicle has been determined (e.g., ID of the road, speed limit on the road), the LT algorithm computes the future positions of the vehicles. 4) The information computed is sent to MOAST as waypoints. The embedded client-server architecture of the Unreal game engine enables USARSim to provide individualized control over multiple robotic systems through discrete socket interfaces. The interfaces provide a generalized representation language that enables the user to query and control the robots’ subsystems. All the communications between the clients (Unreal client and the Controller) and the server are performed through the network. The Unreal Server includes the Unreal Engine, Gamebots to bridge the Unreal Engine with outside applications, the maps and the models (robot models, victims, etc). MOAST first connects to the Unreal Server, and then it sends commands to USARSim to spawn a robot. At this step, MOAST listens to the sensor data and sends commands to control the robot.
|Figure 6. System architecture of USARSim, MOAST and PRIDE.|