Towards enhanced safety and enriched infotainment for connected vehicles: modeling, design and implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automobile manufacturers are actively delivering a new generation of connected vehicles.These vehicles are driving a far-reaching revolution in the modern society.They will not only save a huge amount of lives and property from traffic accidents, but also fundamentally change the way we travel.As an essential building block of connected vehicles, Vehicle-to-Vehicle (V2V) communication technologies have become a major research and development priority of both governments and car manufacturers.Driving safety and In-Vehicle Infotainment (IVI) services are two primary categories of services enabled by V2V communications.They are not only complementary to, but also mutually beneficial to, each other.On one hand, enhancing driving safety is the most critical issue in current traffic systems.A study [1] led by the U.S. Department of Transportation (U.S. DOT) estimated that V2V technologies can avoid 74 percent of car accidents, potentially saving thousands of lives and billions of dollars each year.Infotainment, on the other hand, not only provides extra encouragement to consumers in purchasing V2V devices, but also brings large economic incentives to manufactures in increasing the market penetration of V2V devices.As a result of this increased penetration, each vehicle can gather more information from other surrounding vehicles, leading to a large improvement in the safety of the whole traffic system.In return, enhanced safety allows everyone to better enjoy infotainment services during reassuring journeys.In this sense, safety and infotainment services are mutualistic in the vehicular ecosystem.In this thesis, we focus on two promising V2V technologies, i.e., the Dedicated Short-Range Communication (DSRC) technology for driving safety and the in-cabin Wi-Fi technology for vehicular infotainment.While DSRC has been recognized by U.S. DOT as the enabling technology of the Intelligent Transportation System (ITS), the in-cabin Wi-Fi technology is recently deployed by many car manufacturers, such as General Motors, Ford, BMW, and Mercedes, to enhance travelling experience for both drivers and passengers.We first characterize these new technologies and their unique features with analytical models, and validate these models with extensive simulations.We then manage to improve the performance of these technologies with several novel solutions.In this way, we not only enhance the driving safety, but also provide better Quality of Service (QoS) for IVI.We implement these technologies in evaluation platforms, and conduct both analytical and simulation analyses to evaluate their communication reliability, efficiency and fairness.We ii further implement and test them on real test-beds to demonstrate their large improvements over the state of the art.This thesis represents not only my work in front of the screen, it is also a milestone in almost five years of my study, research and life at McGill University and especially the Cyber-Physical System Laboratory.The path to my doctoral degree was rugged, along with many intellectual challenges and psychological frustrations.I could never overcome them without the valuable help, support, advice and suggestions from many good and honest, brilliant and professional people.Their love and kindness have changed my toughest years into an enjoyable and rewarding experience.First and foremost, I would like to thank my advisor, Professor Xue Liu, for being supportive since day one.This thesis could never be accomplished without his valuable guidance and continuous encouragement.He has been always patient with me, and thoroughly taught me how to become a good researcher and scientist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it