Vehicle-to-vehicle collaborative perception faces fundamental deployment barriers: raw LiDAR data sharing requires over 300 Mbps per vehicle - far exceeding V2X network capacities, while network delays of 80-200ms create dangerous temporal misalignments at highway speeds. We present UniSense, a distributed collaborative perception system that enables efficient and reliable multi-vehicle perception through uncertainty-driven sensor data exchange. Instead of sharing raw sensor data, vehicles exchange compact uncertainty maps that identify regions requiring additional perceptual information. Our key innovations include: (1) a lightweight uncertainty quantification pipeline that runs in real-time on automotive hardware, identifying perception-critical regions while reducing bandwidth requirements by more than 10×, (2) a bandwidth-aware protocol that dynamically adapts data sharing based on network conditions and perception uncertainty, and (3) a selective motion compensation scheme that maintains temporal consistency. We evaluate UniSense through a year-long deployment with 16 roadside LiDAR nodes and autonomous vehicles across our campus. Our experimental results show that UniSense extends reliable perception range from local 80m to 140m, improving accuracy by 1.33× on average, up to 1.73×, over the state-of-the-art baselines, under communication constraints. The code and dataset are available at https://github.com/LetStarFly/UniSense.

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.

HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driving vehicles and roadside infrastructure. This represents a significant improvement of 2.8× and 3× in map accuracy and coverage compared to the state-of-the-art online HD mapping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.

In recent years, the deployment of unmanned vehicle delivery services has increased unprecedentedly, leading to a need for enhanced security due to the risk of leaving high-value packages to an unauthorized third party during pickup or delivery. Existing authentication methods such as QR code and one-time password are inadequate, as they are susceptible to attacks and provide only one-way authentication. This paper, for the first time to our best knowledge, proposes Wave-for-Safe (W4S) --- a novel mutual authentication system that utilizes multi-modal sensors on both the user's smartphone and the unmanned vehicle. W4S uses random hand-waving by the legitimate user to achieve robust authentication by obtaining highly correlated sensory data measured by the Inertial Measurement Unit (IMU) in the smartphone and sensors in the unmanned vehicle (e.g., mmWave radar and camera). We propose several novel methods to overcome challenges such as heterogeneous data processing, asynchronization, and imitating attacks. The prototype is implemented on an unmanned vehicle and various smartphones, and evaluation in different real-world scenarios shows that W4S achieves an equal error rate below 0.013 against various attacks.

Head-mounted wearables are rapidly growing in popularity. However, a gap exists in providing robust voice-related applications like conversation or command control in complex environments, such as competing speakers and strong noises. The compact design of HMWs introduces non-trivial challenges to existing speech enhancement systems that use microphone recording only. In this paper, we handle this problem by using bone vibration conducted through the head skull. The principle is that the accelerometer is widely installed on head-mounted wearables and can capture the clean user's voice. Hence, we develop VibVoice, a lightweight multi-modal speech enhancement system for head-mounted wearables. We design a two-branch encoder-decoder deep neural network to fuse the high-level features of the two modalities and reconstruct clean speech. To address the issue of insufficient paired data for training, we extensively measure the bone conduction effect from a limited dataset to extract the physical impulse function for cross-modal data augmentation. We evaluate VibVoice on a dataset collected in real world and compare it with two state-of-the-art baselines. Results show that VibVoice yields up to 21% better performance in PESQ and up to 26% better performance in SNR compared with the baseline with 72 times less paired data required. We also conduct a user study with 35 participants, in which 87% participants prefer VibVoice compared with the baseline. In addition, VibVoice requires 4 to 31 times less execution time compared with baselines on mobile devices. The demo audio of VibVoice is available at https://www.youtube.com/watch?v=8_-s_C_NGRI.

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.