Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.

Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a novel cooperative edge system that can support concurrent data/compute-intensive deep learning (DL) models for distributed real-time applications such as city-scale traffic monitoring and autonomous driving. First, CoEdge includes a hierarchical DL task scheduling framework that dispatches DL tasks to edge nodes based on their computational profiles, communication overhead, and real-time requirements. Second, CoEdge can dramatically increase the execution efficiency of DL models by batching sensor data and aggregating the inferences of the same model. Finally, we propose a new edge containerization approach that enables an edge node to execute concurrent DL tasks by partitioning the CPU and GPU workloads into different containers. We extensively evaluate CoEdge on a self-deployed smart lamppost testbed on a university campus. Our results show that CoEdge can achieve up to reduction on deadline missing rate compared to baselines.

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-to-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches. A video demo of VIPS based on the lamppost dataset is available at https://youtu.be/zW4oi_EWOu0.

Infrastructure-assisted autonomous driving is an emerging paradigm that aims to make affordable autonomous vehicles a reality. A key technology for realizing this vision is real-time point cloud registration which allows a vehicle to fuse the 3D point clouds generated by its own LiDAR and those on roadside infrastructures such as smart lampposts, which can deliver increased sensing range, more robust object detection, and centimeter-level navigation. Unfortunately, the existing methods for point cloud registration assume two clouds to share a similar perspective and large overlap, which result in significant delay and inaccuracy in real-world infrastructure-assisted driving settings. This paper proposes VI-Eye - the first system that can align vehicle-infrastructure point clouds at centimeter accuracy in real-time. Our key idea is to exploit traffic domain knowledge by detecting a set of key semantic objects including road, lane lines, curbs, and traffic signs. Based on the inherent regular geometries of such semantic objects, VI-Eye extracts a small number of saliency points and leverage them to achieve real-time registration of two point clouds. By allowing vehicles and infrastructures to extract the semantic information in parallel, VI-Eye leads to a highly scalable architecture for infrastructure-assisted autonomous driving. To evaluate the performance of VI-Eye, we collect two new multiview LiDAR point cloud datasets on an indoor autonomous driving testbed and a campus smart lamppost testbed, respectively. They contain total 915 point cloud pairs and cover three roads of 1.12km. Experiment results show that VI-Eye achieves centimeter-level accuracy within around 0.2s, and delivers a 5X improvement in accuracy and 2X speedup over state-of-the-art baselines.

Real-time image-based object tracking from live video is of great importance for several smart city applications like surveillance, intelligent traffic management and autonomous driving. Although recent deep learning systems can achieve satisfactory tracking performance, they incur significant compute overhead, which prevents them from wide adoption on resource-constrained IoT platforms. In this demonstration, we present an Edge Computing system for Real-time object Tracking (ECRT) for resource-constrained devices. The key feature of our system is that it intelligently partitions compute-intensive tasks such as inferencing a convolutional neural network(CNN) into two parts, which are executed locally on an IoT device and/or on the edge server. Moreover, ECRT can minimize the power consumption of IoT devices while taking into consideration the dynamic network environment and user requirement on end to end delay.