Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One particular challenge is the problem of including the Sensing and Perception (S&P) subsystem into the virtual simulation loop in an efficient and effective manner. In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves. We propose a generalized data-driven procedure towards parametric modeling and evaluate it using Apollo, an open-source driving software, and nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an open-source vehicle simulator. Furthermore, we demonstrate the usefulness of PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups. Our virtual tests highlight limitations in the current evaluation metrics, and the proposed approach can help study the impact of perception errors on AV safety.

The worldwide development of Autonomous Vehicles (AVs) has also encouraged the use of software simulators for virtual testing of AVs. However, the effectiveness of the AV simulators is constrained by numerous challenges, such as their computational cost and lack of fidelity in specific areas. In this paper, we describe the modality of virtual testing and its benefits for AV development and validation. Moreover, we summarize and provide an overview of the state-of-the-art AV simulators, their limitations, and the current directions toward improvement.

Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception algorithms are typically designed to measure their accuracy per se and do not account for their impact on the decision making subsystem(s). This limitation does not help developers and third party evaluators to answer a critical question: is the performance of a perception subsystem sufficient for the decision making subsystem to make robust, safe decisions? In this paper, we propose a simulation-based methodology towards answering this question. At the same time, we show how to analyze the impact of different kinds of sensing and perception errors on the behavior of the autonomous system.

Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.

Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking, we target at (indoor) parking garages where the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are unavailable and counting vehicles by crowdsensing is difficult. To this end, we present ParkGauge as a system gauging the congestion level of parking garages; it infers (coarse-grained) parking occupancy from crowdsensed parking characteristics instead of counting the parked vehicles. ParkGauge adopts mostly low-power sensors in the driver's smartphone to determine driving states, contexts and temporal parking characteristics of a garage, including time-to-park and time-in-cruising/queuing. Mining such data collected from a crowd of drivers at various garages yields a good measure of their congestion levels and provide recommendations (in real-time) to drivers coming to these venues.

One of the major challenges during the process of extracting information from multiple spatio-temporal data sources of diverse data types is the matching and fusion of extracted knowledge (e.g. interesting nearby events detected from text, estimated density or flow from a set of geo-coded images). In this demonstration, we present PETRINA ("PErsonalized TRaffic INformation Analytics"), a system that provides traffic-related incident monitoring, mapping, and analytics services. In particular, we showcase two main functionalities: (1) text traffic alert validation based on traffic condition information derived from traffic camera images and (2) traffic incident correlation based on spatio-temporal proximity of different incident types (e.g., accidents and heavy traffic). Despite the fact that the images are sparse (available every three minutes), the regularity makes it possible to validate whether a text traffic alert is outdated or not, and to more accurately estimate the time elapsed and total incident time. Multiple traffic incidents can be grouped together as a single event based on the traffic incident correlation to reduce information redundancy. Such enhanced real-time traffic information enables PETRINA to offer services such as dynamic routing with traffic incident advices, spatiotemporal traffic incident visual analytics, and congestion analysis.