Revolution on Wheels: A Survey on the Positive and Negative Impacts of Connected and Automated Vehicles in Era of Mixed Autonomy
Bibliographic record
Abstract
With the development of autonomous driving technology, it is foreseeable that connected and automated vehicles (CAVs) will be fully popularized in people’s lives. During this process, transportation systems are expected to evolve into the era of mixed autonomy, where CAVs and human-driven vehicles (HDVs) coexist in road networks and share available road resources. To materialize the much-anticipated potential of CAVs, a thorough understanding of CAVs’ effects on transportation systems is indispensable. On the one hand, attributing to advanced sensing, communication, and computation capabilities, CAVs provide opportunities to enhance mixed traffic safety, improve energy savings and suppress shockwave spread. On the other hand, due to advantages in large-scale information and cloud-computing resources, CAVs have the ability to occupy more road resources compared with HDVs, resulting in a reduction in the travel efficiency of HDVs, and even of the entire transportation systems. In this article, by clarifying the key differences between HDVs and CAVs, we comprehensively review the potential impacts of CAVs when they are appearing on road networks coexisting with HDVs. It can be regarded as the first-of-its-kind paper that systematically overviews the impacts of CAVs in the era of mixed autonomy on both positive and negative emotions. Specifically, the main focuses of this article are: 1) what are the key differences between CAVs and HDVs? 2) what are the positive impacts of CAVs’ appearance on mixed traffic systems? 3) will the introduction of CAVs cause some negative effects simultaneously? and 4) what kinds of strategies should be employed to relieve these negative effects? Hopefully, this article can not only call for an objective attitude toward the introduction of CAVs, but also provide foresighted advice to address possible challenges during the popularization of CAVs, so as to create a cooperative, safe, and efficient mixed traffic ecosystem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".