Sidi Lu

[1] Integrated Vehicle-EdgeServer-Cloud Framework for Compressive Imaging Processing

Nokia Bell Lab – The CAR Lab Official Collaboration

My role: Project leader

·Introduce the technologies of temporal compressive sensing into the connected vehicle applications. which provides a new and novel direction to achieve faster video processing by executing models on optically compressed video images, i.e., a small number of linear projections of the original video images.

·Experiment results proved that the detection accuracy on the compressed video images (named measurements) is close to the accuracy on reconstructed videos and comparable to the true value, which proves the effectiveness of employing DNN models on the compressed video images. This is the first work that provides a new direction to achieve fast DNN inference.

 

[2] Reinforcement Learning for Adaptive Video Compressive Sensing

Collaborate with Nokia Bell Lab and Northwestern University

My role: Project leader

·When employing object detection models on the compressed video images, a higher compression ratio (B) is desired to obtain faster inference speed and less transmission costs. However, there is a significant trade-off between B, detection accuracy, and the quality of the reconstructed video.

·One research gap in previous studies is how to adapt the compression ratio under different scenarios. We fill this gap utilizing reinforcement learning (RL) and various DNNs to automatically choose optimal B for different application scenarios. This work takes the technology one step further towards real applications of compressive images.

 

[3] Implementation and Evaluation of an Edge Computing Framework for Connected Vehicles

Collaborate with Nokia Bell Lab and Northwestern University

My role: Project leader

·The proposed Vehicle-EdgeServer-Cloud (VEC) framework is fully evaluated using our designed roadside platform and outdoor delivery vehicle.

·Our framework has a potential of 18× reduction on the bandwidth requirement, i.e., it can work stably with limited bandwidth of around 320KB/s. This demonstrates its extremely promising applications for connected vehicles (CVs).

 

[4] Collaborative Learning on the Edges

Research assistant at Wayne State University

My role: Project leader

·Propose a collaborative learning framework on the edges, dubbed CLONE. Two application scenarios for CLONE were explored, including CLONE in the training stage (CLONE_training) and CLONE in the inference stage (CLONE_inference).

·Distributed Electric Vehicle Battery Failure Prediction: As to CLONE_training, we choose the failure of electric vehicle (EV) battery and associated accessories as our case study to show how the collaborative framework can accurately predict failures to ensure sustainable and reliable driving in a collaborative fashion.

·Multi-target Multi-camera Tracking: As to CLONE_inference, we choose multi-target multi-camera tracking as another case study to prove that the collaborative framework can also keep tracking customers based on eight different cameras in a grocery store.

 

[5] Extensible and Flexible Middleware for Edge Service

Research assistant at Wayne State University

My role: Project leader

·Propose an extensible and flexible middleware to manage the execution of vehicle services, with four key features: i) on-demand model switch, ii) function consolidation and deduplication to eliminate duplicate copies of repeating functions and maximize the reusability of vehicle services, iii) build event-driven applications to reduce workload, and iv) dynamic workflow customization which enables customizing workflow to extend the functionality.

·Our experiment results show that EdgeWare accelerates the execution of services about 2.6× faster compared to the silo approach and save CPU and memory utilization up to around 50% and 17% respectively.

 

[6] Disk Failure Prediction

Collaborate with Northeastern University

My role: Project leader

·This study covers the disk and server data measured and collected at a large data center. Over, the dataset spans over 64 data center sites, 10,000 server racks and 380,000 hard disks for roughly 70 days. This corresponds to roughly 2.6 million device hours.

·We discovered that performance metrics are good indicators of disk failures and location markers can improve the accuracy of disk failure prediction.

·We also train machine learning models including neural network models to predict disk failures with 0.95 F-measure and 0.95 MCC for 10 days prediction horizon.

[7] Multimodal-based Campus-wide Pandemic Forecasting

Research assistant at Wayne State University

My role: Project leader

·Build a multimodal-based COVID-19 pandemic forecasting platform steering by video, audio, and tweets for Wayne State University to minimize the impact of COVID-19 after resuming academic activities.

·Train diverse models to extract meaningful information from video, audio, and tweets by i) detecting and counting face masks, ii) detecting and counting cough for potential infected cases, and iii) conducting sentiment analysis based on COVID-19 related tweets.

·Fed the multimodal analysis results together with daily confirmed cases data and social distancing metrics into the LSTM model to predict the daily increase rate of confirmed cases for the next seventh day.

 

[8] Lane Detection/Prediction Enhancement under Challenging Scenarios

Intern project in GM

My role: Project leader

·Focus on the lane detection/prediction enhancement under challenging scenarios with severe occlusion and extreme lighting conditions (e.g., rainy and snowy days) when the perception system of the connected and autonomous vehicle (CAV) loses the lane detection and urgently needs higher-level semantic analysis of lane markings.

[9] A comparison of End-to-End Architectures for Connected Vehicles

Toyota InfoTeach Labs - The CAR Lab Official Collaboration

My role: Project leader

·Examine different types of architectures for vehicle computing, including centralized architecture, decentralized architecture, publish/subscribe architecture, and broadcast architecture.

·Classify essential vehicle applications and discuss their performance requirements (e.g., communication mode, critical latency, data rate per vehicle, and communication range).

·Choose software over-the-air (OTA) update as our case study and propose an edge-based architecture for connected vehicles, named EdgeArC.

 

[10] Teleoperation Technologies for Enhancing Connected and Autonomous Vehicles

Research Assistant at Wayne State University

My role: Project leader

·Fully autonomous driving is not yet completely feasible. Teleoperation promises to be an important interim solution for bridging the gap between current autonomous driving capabilities and the widespread adoption of autonomous vehicles, where a remote operator can supervise vehicles with manual intervention via wireless communication.

·We explore technical considerations, enhancement techniques, evaluation criteria, and summarize research efforts of automotive participants and teleoperation use cases.

 

[11] The Emergency of Vehicle Computing

Research Assistant at Wayne State University

My role: Project leader (vision paper)

·With the proliferation of onboard computing and communication capabilities, we envision that connected vehicles will be serving as a mobile computing platform in addition to their conventional transportation role for the next century.

·We also discuss vehicle computing from several aspects, including several case studies, key enabling technologies, a potential business model, a general computing framework, and open challenges.

 

[12] Cross-Layer Computing Scheduling for Heterogeneous Bare Metal Clusters: A Case Study for Connected and Autonomous Vehicles

Zenlayer – The CAR Lab Official Collaboration

My role: Project leader

·Develop automatic cross-layer computing scheduling approaches for the whole stack from the distributed and heterogeneous infrastructure (i.e., Bare Metal clusters) to the virtual machine/container orchestration platforms (e.g., Kubernetes) and all the way to applications (e.g., games and short videos).

 

Highlights: Served as the instructor (part-time faculty) and teaching assistant for six semesters in the Department of Computer Science at Wayne State University, including teaching junior/senior undergraduate courses and research-oriented graduate courses. The faculty individual report denoted the mean and median ratings of my Student Evaluation of Teaching (SET) are 4.4/5 and 5/5, respectively [Link].

 

Instructor (Part-time Faculty), CSC 4920 Introduction to Computer Networks                                           ;Fall 2022

Department of Computer Science, Wayne State University

·         Preparing lecture materials, initiating discussions, holding office hours, and designing and grading in-class quizzes, assignments, and exams for 15 undergraduate students.

 

Teaching Assistant, CSC 5250 Network Distributed and Concurrent Programming                                  Fall 2018

Department of Computer Science, Wayne State University

·         Elaborating on basic principles, moderating course presentation sessions, and grading course assessments for around 10 graduate students.

 

Teaching Assistant, CSC 4996 Senior Project and Computer Ethics                                                      Winter 2018

Department of Computer Science, Wayne State University

·         Guiding three teams of 12 undergraduate students to complete their graduation capstone projects.

·         Coaching teams on establishing appropriate project milestones, holding weekly meetings, and providing technical instruction to students.

·         Managing projects including CarPool iOS Application, Directed Graph Viewer (Paper Trail Web Application), and GM Big Data Analytics.

 

Instructor, CSC 1101 Problem Solving and Programming Laboratory                  Fall 2016, Winter 2017, Fall 2017

Department of Computer Science, Wayne State University

·          Preparing lectures slides, holding office hours, and designing and grading hands-on exercises and assignments for around 20 undergraduate students to understand programming basics with C++.

·         Student Evaluation of Teaching (SET): a mean score of 4.5/5 and a median score of 5/5 [Link].