book/book chapter
Preprint Journal Article
The rapid advancement of Autonomous Vehicles (AVs), exemplified by companies like Waymo and Cruise offering 24/7 paid taxi services, highlights the paramount importance of ensuring AVs’ compliance with various policies, such as safety regulations, traffic rules, and mission directives. Despite significant progress in the development of Autonomous Driving System (ADS) testing tools, there has been a notable absence of research on attributing the causes of driving violations. Counterfactual causality analysis has emerged as a promising approach for identifying the root cause of program failures. While it has demonstrated effectiveness in pinpointing error-inducing inputs, its direct application to the AV context to determine which computation result, generated by which component, serves as the root cause poses a considerable challenge. A key obstacle lies in our inability to straightforwardly eliminate the influence of a specific internal message to establish the causal relationship between the output of each component and a system-level driving violation. In this work, we propose a novel driving violation cause analysis (DVCA) tool. We design idealized component substitutes to enable counterfactual analysis of ADS components by leveraging the unique opportunity provided by the simulation. We evaluate our tool on a benchmark with real bugs and injected faults. The results show that our tool can achieve perfect component-level attribution accuracy (100%) and almost (>98%) perfect message-level accuracy. Our tool can reduce the debugging scope from hundreds of complicated interdependent messages to one single computation result generated by one component.
published journal article
conference paper
This paper addresses the problem of dynamically matching automated vehicles (AVs) to open traveler requests in a large-scale automated-mobility-on-demand (AMOD) simulation framework. While optimization-based matching strategies based on the linear assignment problem formulation significantly outperform simple heuristic strategies (e.g. nearest neighbor), the scalability of the assignment problem limits its applicability to large problem instances. This study proposes a fleet dispatching strategy to dynamically assign AVs to travelers that involves the assignment problem formulation but restricts the decision space to reduce computational time. First, we significantly trim the decision space via only considering the k-closest open requests around each idle vehicle or k-closest idle vehicles around each open request. Second, we only calculate point-to-point shortest paths for vehicles and travelers that are close in spatial proximity. For vehicles and travelers that are not close in proximity, we use zone-to-zone travel time estimates. This study embeds the proposed AV fleet dispatching strategy within Polaris-an agent-based transportation simulation modeling framework. Within Polaris, the restricted fleet dispatching strategy proposed in this significantly outperforms (i) existing large-scale strategies in terms of fleet performance and (ii) the unrestricted assignment problem strategy in terms of computational performance.
conference paper
published journal article
research report
The goal of this research project is to improve the operational efficiency of shared-ride mobility-on-demand services (SRMoDS). SRMoDS ranging from UberPool to micro-transit have the potential to provide travelers mobility benefits that are comparable to existing ride-hailing services without shared rides such as UberX, but at a lower cost and with fewer harmful externalities. To meet the project’s goal, this study proposes a bi-criteria network pathfinding approach that considers proximity to potential future traveler requests in addition to travel time. This pathfinding approach was built on top of a state-of-the-art dynamic vehicle routing and matching modules. The study tests the proposed pathfinding approach using the network of the City of Anaheim. The results indicate that the proposed bi-criteria pathfinding can potentially reduce both traveler waiting and in-vehicle travel time; however, the effectiveness depends on several factors. Important factors include the relative supply-demand imbalance as well as several hyperparameters in the optimization-based control policy. Moreover, the results indicate that the bi-criteria policy is only advisable when the SRMoDS vehicle has one or fewer in-vehicle passengers. Although the operational benefits found in this study are relatively small, future research efforts related to tuning hyperparameters should allow bi-criteria pathfinding to significantly improve SRMoDS.
conference paper
conference paper
conference paper