Tsz-Chiu Au

SHOP2: an HTN planning system

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Applications of SHOP and SHOP2

We design the simple hierarchical ordered planner (SHOP) and its successor, SHOP2, with two goals in mind: to investigate research issues in automated planning and to provide some simple, practical planning tools. SHOP and SHOP2 are based on a planning formalism called hierarchical task network planning. SHOP and SHOP2 use a search-control strategy called ordered task decomposition, which breaks tasks into subtasks and generates the plans actions in the same order that the plan executor executes them. So, throughout the planning process, the planner can tell what the state of the world at each step of the plan.

Automated Intersection Control: Performance of Future Innovation Versus Current Traffic Signal Control

Congestion is one of the biggest challenges faced by the transportation community; congestion accounted for an estimated

Bringing simulation to life: A mixed reality autonomous intersection

87.2 billion in losses in 2007 alone. Transportation professionals need to go beyond capacity expansion projects and explore novel strategies to mitigate traffic congestion. Automated intersection management is a novel strategy that has the potential to greatly reduce intersection delay and improve safety. Although the implementation of such a system is contingent on the development of automated vehicles, competitions such as the Grand Challenge and Urban Challenge of the Defense Advanced Research Projects Agency have shown that this technology is feasible and will be available. Therefore, the development of the infrastructure and associated control methods required to exploit fully the benefits of such technology at the system level is critical. This research explores one such innovative strategy, an automated intersection control protocol based on a first-come, first-served (FCFS) reservation system. The FCFS reservation system was shown to reduce intersection delay significantly by exploiting the features of autonomous vehicles. Microscopic simulation experimental results showed that the FCFS reservation system significantly outperformed a traditional traffic signal in reducing delay.

Web service composition with volatile information

Fully autonomous vehicles are technologically feasible with the current generation of hardware, as demonstrated by recent robot car competitions. Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that with autonomous vehicles it is possible to make intersection control much more efficient than the traditional control mechanisms such as traffic signals and stop signs. The protocol, however, has only been tested in simulation and has not been evaluated with real autonomous vehicles. To realistically test the protocol, we implemented a mixed reality platform on which an autonomous vehicle can interact with multiple virtual vehicles in a simulation at a real intersection in real time. From this platform we validated realistic parameters for our autonomous vehicle to safely traverse an intersection in AIM. We present several techniques to improve efficiency and show that the AIM protocol can still outperform traffic signals and stop signs even if the cars are not as precisely controllable as has been assumed in previous studies.

On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework

In many Web service composition problems, information may be needed from Web services during the composition process. Existing research on Web service composition (WSC) procedures has generally assumed that this information will not change. We describe two ways to take such WSC procedures and systematically modify them to deal with volatile information. The black-box approach requires no knowledge of the WSC procedures internals: it places a wrapper around the WSC procedure to deal with volatile information. The gray-box approach requires partial information of those internals, in order to insert coding to perform certain bookkeeping operations. We show theoretically that both approaches work correctly. We present experimental results showing that the WSC procedures produced by the gray-box approach can run much faster than the ones produced by the black-box approach.

A Platform for Evaluating Autonomous Intersection Management Policies

In this paper we present an algorithm called DerUCP, which can be regarded as a general model for plan adaptation using Derivational Analogy. Using DerUCP, we show that previous results on the complexity of plan adaptation do not apply to Derivational Analogy. We also show that Derivational Analogy can potentially produce exponential reductions in the size of the search space generated by a planning system.

Dynamic lane reversal in traffic management

There is a significant push towards greater vehicular autonomy on roads to increase convenience and improve overall driver experience. To enable this autonomy, it is imperative that cyber-physical infrastructure be deployed to enable efficient control and communication. An essential component of such road instrumentation is intersection management. This paper develops an intersection management platform that provides the sensing and communication infrastructure needed to enable efficient intersection management policies. The test bed, located in a indoor laboratory, consists of an intersection and multiple robotic vehicles that can sense and communicate. Whereas traditional approaches to intersection management rely on simulations, this test bed enables the first realistic evaluation of several intersection management policies. Six simple but practical centralized and distributed policies are evaluated and compared against the current state of the art, i.e., traffic signals and stop signs. Through extensive experimentation, this paper concludes that, in the scenario tested, even a simple coordinated management policy can halve vehicular delay, while improving the aggregate traversal time of the intersection by 169%.

Evasion planning for autonomous vehicles at intersections

Contraflow lane reversal — the reversal of lanes in order to temporarily increase the capacity of congested roads — can effectively mitigate traffic congestion during rush hour and emergency evacuation. However, contraflow lane reversal deployed in several cities are designed for specific traffic patterns at specific hours, and do not adapt to fluctuations in actual traffic. Motivated by recent advances in autonomous vehicle technology, we propose a framework for dynamic lane reversal in which the lane directionality is updated quickly and automatically in response to instantaneous traffic conditions recorded by traffic sensors. We analyze the conditions under which dynamic lane reversal is effective and propose an integer linear programming formulation and a bi-level programming formulation to compute the optimal lane reversal configuration that maximizes the traffic flow. In our experiments, active contraflow increases network efficiency by 72%.

Reactive Query Policies: A Formalism for Planning with Volatile External Information

Autonomous intersection management (AIM) is a new intersection control protocol that exploits the capabilities of autonomous vehicles to control traffic at intersections in a way better than traffic signals and stop signs. A key assumption of this protocol is that vehicles can always follow their trajectories. But mechanical failures can occur in real life, causing vehicles to deviate from their trajectories. A previous approach for handling mechanical failure was to prevent vehicles from entering the intersection after the failure. However, this approach cannot prevent collisions among vehicles already in the intersection or too close to stop because (1) the lack of coordination among vehicles can cause collisions during the execution of evasive actions; and (2) the intersection may not have enough room for evasive actions. In this paper, we propose a preemptive approach that pre-computes evasion plans for several common types of mechanical failures before vehicles enter an intersection. This preemptive approach is necessary because there are situations in which vehicles cannot evade without pre-allocation of space for evasion. We present a modified AIM protocol and demonstrate the effectiveness of evasion plan execution on a miniature autonomous intersection testbed.