Vehicle function offloading has been an active research topic in the connected and automated vehicles (CAV) domain. Mobile edge computing can execute sophisticated algorithms on abundant computing resources, leading to superior accuracy for vehicle system state estimation. On the other hand, cellular network latency causes edge-computed information to be obsolete.In this paper, by focusing on camera-based object tracking applications, we develop a novel state fusion framework that not only achieves the benefits (enhanced detection accuracy) but also mitigates the disadvantages (unpredictable network latency) of edge computing. This is achieved by carefully managing the system state consistency between the vehicle onboard system and the remote edge system through our novel backward-and-forward algorithms. We evaluate our system by comparing our method with the edge-only and onboard-only counterparts through extensive empirical experiments. The presented framework improves the accuracy of camera-based perception by at least 2x compared to traditional techniques, with an average response time of 48.13 ms (strictly less than the mission-critical latency threshold of 100 ms [1]).

Vehicular applications are becoming increasingly complex and resource hungry (e.g. autonomous driving). Today, they run entirely on the vehicle, which is a costly solution that also imposes undesirable resource constraints. This paper uses Simultaneous Localization and Mapping (SLAM) as an example application to explore how these applications can instead leverage edge clouds, utilizing their inexpensive and elastic resource pool. This is challenging as these applications are often latency-sensitive and mission-critical. They also process high-bandwidth sensor data streams and maintain large, complex data structures. As a result, traditional offloading techniques generate too much traffic, incurring high delay. To overcome these challenges, we designed CloudSLAM. It partitions SLAM between the vehicle and the edge. To manage the complex, replicated SLAM state, we propose a new consistency model, Output-driven Consistency, that allows us to maintain a level of consistency that is sufficient for accurate SLAM output while minimizing network traffic. This paper motivates and describes our offloading design and discusses the results of an extensive performance evaluation of a CloudSLAM prototype based on ORB-SLAM.

Driver assistance and vehicular automation would greatly benefit from uninterrupted lane-level vehicle positioning, especially in challenging environments like metropolitan cities. In this paper, we explore whether the WiFi Fine Time Measurement (FTM) protocol, with its robust, accurate ranging capability, can complement current GPS and odometry systems to achieve lane-level positioning in urban canyons. We introduce Wi-Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by coherently fusing WiFi FTMs, GPS, and vehicle odometry information together. Wi-Go also adaptively controls the FTM messaging rate from clients to prevent high bandwidth usage and congestion, while maximizing the tracking accuracy. Wi-Go achieves lane-level vehicle positioning (1.3 m median and 2.9 m 90-percentile error), an order of magnitude improvement over vehicle built-in GPS, through vehicle experiments in the urban canyons of Manhattan, New York City, as well as in suburban areas (0.8 m median and 3.2 m 90-percentile error).

Academic and industry research has argued for supporting WiFi time-of-flight measurements to improve WiFi localization. The IEEE 802.11-2016 now includes a Fine Time Measurement (FTM) protocol for WiFi ranging, and several WiFi chipsets offer hardware support albeit without fully functional open software. This paper introduces an open platform for experimenting with fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems. We analyze the key factors and parameters that affect the ranging performance and revisit standard error correction techniques for WiFi time-based ranging system. The results confirm that meter-level ranging accuracy is possible as promised, but the measurements also show that this can only be consistently achieved in low-multipath environments such as open outdoor spaces or with denser access point deployments to enable ranging at or above 80 MHz bandwidth.

With the advent of the new IEEE 802.11p DSRC/WAVE radios, Vehicle-to-Vehicle (V2V) communications is poised for a dramatic leap. A canonical application for these future vehicular networks is the detection and notification of anomalous road events (e.g., potholes, bumps, icy road patches, etc.). We present the Road Information Sharing Architecture (RISA), the first distributed approach to road condition detection and dissemination for vehicular networks. RISA provides for the in-network aggregation and dissemination of event information detected by multiple vehicles in a timely manner for improved information reliability and bandwidth efficiency. RISA uses a novel Time-Decay Sequential Hypothesis Testing (TD-SHT) approach in which event information from multiple sources is combined with time-varying beliefs. We describe our implementation of RISA which has been deployed and tested on a fleet of vehicles on-site at the GM Warren Technical Center in Michigan. We further provide a comprehensive evaluation of the aggregation mechanism using emulation of the RISA code on real vehicular mobility traces.

This paper studies the problem of bulk data transfer between a pair of moving vehicles as they encounter, using IEEE 802.11p (Dedicated Short Range Communication, or DSRC) radios. Comparing to the well-studied problem of “Drive-Through Internet”, enabling an efficient, reliable and fair data transfer between two fast-moving vehicles could be even more challenging because the effective link duration is halved, vehicle-to-vehicle channel is unpredictable and DSRC is a new technology. We design an Encounter Transfer Protocol (ETP) which is suitable for this vehicle encounter case. We introduce two new components which are able to improve the data transfer performance in such a challenging environment: (1) we advocate an enhanced window adjustment policy called BIBD(Bimodal Increase, Bimodal Decrease) which is able to quickly adapt to fast-changing channel conditions and stabilize afterwards. We experimentally demonstrate the BIBD policy outperforms other policies, including commonly used AIMD policy. (2) We also suggest a variety of enhancement techniques that not only fully utilize the precious link duration as vehicles encounter but also aggressively compensate packet drops caused by fading channel. Using a small fleet of DSRC-equipped vehicles, we experimentally demonstrate that ETP is able to at least double the throughput of TCP as both vehicles are moving, and improve performance by about 20-50% as only one vehicle is moving. Our experiments also show that ETP fairly allocates bandwidth resource under stressful network congestion scenarios.