Multi-vehicle collaborative sensing has emerged as a new paradigm to boost the safety of autonomous vehicles. The cornerstone of this vision is the real-time and accurate registration of mmWave radar point clouds among multiple vehicles. To accomplish this, we designmmReg, an innovative system capable of achieving centimeter-level and real-time sensing fusion between vehicles.mmRegconsists of three major components: (i) aSAR imaging-driven point cloud generationcomponent leverages SAR imaging to image sparse and disordered radar point clouds to generate high-quality point clouds; (ii) amotion-aware frame synchronizationcomponent can achieve the spatio-temporal alignment of point clouds between vehicles for effectively mitigating the impact of asynchronous radar frames; (iii) ashared object-based registrationcomponent can capture and understand the unique global position of shared objects, supporting real-time and accurate registration. We implement and evaluatemmRegon CARLA and real-world campus datasets. The results demonstrate thatmmRegcan improve the vehicle’s sensing range by 117% in an average of 99.91 ms, achieving a 4.82x improvement in accuracy.

Live volumetric video streaming provides immersive and interactive experiences. However, state-of-the-art live streaming systems need excessive bandwidth, exceeding the bandwidth capabilities of common mobile networks. A basic solution is to eliminate the redundancy of the original captured data and compress the created volumetric video. However, existing approaches either incorrectly discard normal data or encode the data inefficiently. In this paper, we propose LiVo, a bandwidth-efficient Live Volumetric video streaming system that comprises (i) a compact capture method that efficiently eliminates superfluous points from multiple partial point clouds, and (ii) a compact attribute encoding method that eliminates the spatial-temporal redundancy by proximity-reserved attribute mapping. We implement a prototype of LiVo using commodity devices and extensively evaluate its performance. Our results demonstrate that LiVo achieves bandwidth-efficient live volumetric video streaming and supports transmission under common mobile networks for the first time. Compared with state-of-the-art systems, LiVo reduces bandwidth consumption by at least 47.93%.

Millimeter-wave (mmWave) radar enables privacy-preserving gesture recognition but suffers from limited training data, particularly for lying postures. Existing mmWave radar data generation methods are ineffective due to insufficient 2D video data. To this end, we design a novel system named Venus to generate realistic radar data for lying postures using few 2D videos, which addresses two key challenges including i) the simulation of diverse reflected signals and ii) few real-world data leading to low data fidelity. Venus consists of two key components: (i) a gesture sequence generation and signal simulation network, which combines several key modules, movement information extractor, spatio-temporal latent diffusion model, and mmWave signal simulator, to generate diverse gesture vertex sequences under certain conditions and simulate signal propagation characteristics to obtain coarse radar data; (ii) a meta-learning domain adaption network generates realistic radar data with few real-world data via ''meta-learning'' strategy. Extensive experiments on both generated and self-collected datasets demonstrate that Venus significantly outperforms state-of-the-art methods in recognizing gestures performed in lying postures.

The increasingly prevalent livecast streaming causes expensive bandwidth costs and delivery capacity challenges for the content delivery network (CDN) service. As an emerging paradigm, edge computing offers new opportunities to address this issue. The existing works are limited to the data volume pricing model. In contrast, we focus on the 95th-percentile pricing model, which is adopted by many large-scale livecast systems. We propose a Cost-aware Edge-assisted Livecast system (CEL) to minimize the bandwidth cost, consisting of two components: 1) the Shapley values are leveraged to model the actual bandwidth costs for the CDN and edge servers in different time slots, together with acceleration technologies for fast Shapley value estimation and 2) a greedy request scheduling algorithm with theoretical guarantees is proposed to solve the online request scheduling problem, which is NP-hard. Based on real-world data from an operational livecast system, our experiments demonstrate thatCELis time-efficient and achieves at least 14.81% bandwidth cost savings compared with four state-of-the-art methods.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps bandwidth to transmit volumetric videos, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-calledexplicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This paper introduces a new concept calledimplicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments on public datasets show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.64× and 3.34× improvement compared with two state-of-the-art baselines, respectively.

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, i.e., adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95$^{\rm \textit {th}}$percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose TrafAda, a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed TrafAda on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that TrafAda improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.

Crowd logistics, as an emerging delivery paradigm, provides a cost-efficient way of leveraging crowdsourcing couriers to help express enterprises to match the surging delivery demands that are hard to be addressed by regular couriers only during online mega sale days. However, it is a challenging problem how to recruit an appropriate number of crowdsourcing couriers and assign an appropriate number of parcels to them and regular couriers, as many practical issues need to be considered, such as the dynamic competitive crowdsourcing market, the turnover of crowdsourcing couriers, and unique workload patterns of regular couriers. We design a crowdsourcing-assisted express system called COME to coordinate crowdsourcing and regular couriers for minimizing the overall cost of labor payment and parcel backlog. In COME, we design an Opponent-Aware Reinforcement Learning model to learn the recruitment difficulty in a competitive crowdsourcing market to make an appropriate recruitment plan, and design a four-staged approach to make an appropriate parcel assignment plan, which can address not only the dynamic recruitment difficulty but also the dynamic number of couriers. We have implemented and deployed COME on a real-world crowdsourcing-assisted express system in China involving 1358 delivery stations over 145 cities, and extensively evaluated it with a four-year real-world dataset, demonstrating its great advantage over other alternative solutions and showing high feasibility and generality.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-called explicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This work introduces a new concept called implicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.37x improvement compared with two baselines.

For a new city that is committed to promoting Electric Vehicles (EVs), it is significant to plan the public charging infrastructure where charging demands are high. However, it is difficult to predict charging demands before the actual deployment of EV chargers for lack of operational data, resulting in a deadlock. A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city. However, the demand prediction and charger planning depend on each other, and it is required to re-train the prediction model to eliminate the negative transfer between cities for each varied charger plan, leading to the unacceptable time complexity. To this end, we design an effective solution of S imultaneous Demand P rediction A nd P lanning ( SPAP ): discriminative features are extracted from multi-source data, and fed into an Attention-based Spatial-Temporal City Domain Adaptation Network ( AST-CDAN ) for cross-city demand prediction; a novel Transfer Iterative Optimization ( TIO ) algorithm is designed for charger planning by iteratively utilizing AST-CDAN and a charger plan fine-tuning algorithm. Extensive experiments on real-world datasets collected from three cities in China validate the effectiveness and efficiency of SPAP . Specially, SPAP improves at most 72.5% revenue compared with the real-world charger deployment.