Collaborative Perception enables multiple agents, such as autonomous vehicles and infrastructure, to share sensor data via vehicular networks so that each agent gains an extended sensing range and better perception quality. Despite its promising benefits, realizing the full potential of such systems faces significant challenges due to inherent imperfections in underlying system layers, consisting of network layer imperfections and hardware-level noises. Such imperfections and noises include packet loss in vehicular networks, localization errors from GPS measurements, and synchronization errors caused by clock deviation and network latency. To address these challenges, we propose a novel end-to-end collaborative perception framework, SCORPION, that harnesses the AI co-design of the application layer and system layer to tackle the aforementioned imperfections. SCORPION consists of three main components: lost bird’s eye view feature reconstruction (L-BEV-R) recovers lost spatial features during lossy V2X communication, while deformable spatial cross attention (DSCA) and temporal alignment (TA) compensate for localization and synchronization errors in feature fusion. Experimental results on both synthetic and real-world collaborative 3D object detection datasets demonstrate that SCORPION advances the state-of-the-art collaborative perception methods by 5.9 - 13.2 absolute AP on both standard and noisy scenarios.

Neural-enhanced video streaming (e.g., super-resolution) is an ongoing revolution which can provide extremely high-quality video streaming services breaking the restriction of bandwidth. However, such enhancements require intense computation power that is not affordable for a single mobile device, which hinders their real-world deployment. To address the limitation, we propose OASIS, the first system that facilitates multiple users in close proximity to execute intense neural-enhanced video streaming in realtime. To this end, OASIS intelligently distributes computation tasks among multiple mobile devices, selects appropriate video bitrates and super-resolution models, and optimizes video chunk delivery. As a result, the expensive neural-enhanced streaming is done through distributed collaboration, achieving optimal quality of experience (QoE). We implement and evaluate OASIS on commodity smartphones from different vendors, under various network and computation conditions. Extensive experiments demonstrate the high efficiency of OASIS: it improves the video streaming QoE by 40%-200% and reduces each participant's energy consumption by 60% when the system scales up from a single device to six devices.

Collaborative Vehicular Perception (CVP) enables connected and autonomous vehicles (CAVs) to cooperatively extend their views through wirelessly sharing their sensor data. Existing CVP systems employ either a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) view exchange paradigm. In this paper, we advocate a hybrid CVP design: our developed system, Harbor, employs V2I as its fundamental underlying framework, and opportunistically employs V2V to boost the performance. In Harbor, vehicles (helpers) may serve as relays to assist other vehicles (helpees) in reaching an edge node, which performs sensor data merging to produce the extended view. We judiciously partition the workload between the edge and vehicles, develop a robust helper-helpee assignment model, and solve it efficiently at runtime. We conduct both real-world tests and large-scale emulation experiments using two prevailing CAV applications: drivable space detection and object detection. Our real-world evaluation conducted at one of the world's first purpose-built autonomous driving testbeds demonstrates that Harbor outperforms state-of-the-art V2V- or V2I-only CVP schemes by up to 36% in detection accuracy, resulting in significantly fewer collisions under dangerous driving scenarios.

Cooperative perception significantly enhances the perception performance of connected autonomous vehicles. Instead of purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data to perceive the environment collaboratively. Through our study, we realize that the performance of cooperative perception systems is limited in real-world deployment due to (1) out-of-sync sensor data during data fusion and (2) inaccurate localization of occluded areas. To address these challenges, we develop RAO, an innovative, effective, and lightweight cooperative perception system that merges asynchronous sensor data from different vehicles through our novel designs of motion-compensated occupancy flow prediction and on-demand data sharing, improving both the accuracy and coverage of the perception system. Our extensive evaluation, including real-world and emulation-based experiments, demonstrates that RAO outperforms state-of-the-art solutions by more than 34% in perception coverage and by up to 14% in perception accuracy, especially when asynchronous sensor data is present. RAO consistently performs well across a wide variety of map topologies and driving scenarios. RAO incurs negligible additional latency (8.5 ms) and low data transmission overhead (10.9 KB per frame), making cooperative perception feasible.

With 5G's support for diverse radio bands and different deployment modes, e.g., standalone (SA) vs. non-standalone (NSA), mobility management - especially the handover process - becomes far more complex. Measurement studies have shown that frequent handovers cause wild fluctuations in 5G throughput, and worst, service outages. Through a cross-country (6,200 km+) driving trip, we conduct in-depth measurements to study the current 5G mobility management practices adopted by three major U.S. carriers. Using this rich dataset, we carry out a systematic analysis to uncover the handover mechanisms employed by 5G carriers, and compare them along several dimensions such as (4G vs. 5G) radio technologies, radio (low-, mid- & high-)bands, and deployment (SA vs. NSA) modes. We further quantify the impact of mobility on application performance, power consumption, and signaling overheads. We identify key challenges facing today's NSA 5G deployments which result in unnecessary handovers and reduced coverage. Finally, we design a holistic handover prediction system Prognos and demonstrate its ability to improve QoE for two 5G applications 16K panoramic VoD and realtime volumetric video streaming. We have released the artifacts of our study at https://github.com/SIGCOMM22-5GMobility/artifact.

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at https://github.com/SIGCOMM21-5G/artifact.