Yes, in principle—but that possibility is not proof that autonomous cars generally copy the worst behavior of human drivers. Systems trained with imitation learning can absorb shortcuts, correlations and mistakes in the demonstrations they see. If the training examples contain unsafe behavior, or if the system is deployed in conditions unlike its training environment, the resulting policy may behave badly. The evidence available today supports a real engineering risk, not a finding that driverless fleets routinely speed, drive drunk or text while driving.
What “learning human driving” actually means
Imitation learning trains a driving policy from demonstrations. Instead of programming every steering, braking and lane-choice rule by hand, engineers show a model examples of observations and the actions taken in those situations. The model then learns a mapping from what it sees—road geometry, traffic, signs, pedestrians and vehicle motion—to an action such as steering, accelerating or braking.
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The demonstrations need not come only from the vehicle’s designated human driver. Research can also treat trajectories of nearby cars as examples of how traffic behaves. A 2025 University of California, Berkeley report by Yasin Sonmez, Hanna Krasowski and Murat Arcak proposes selecting informative and diverse trajectories from surrounding traffic to augment training data. Its experiments use the PLUTO planning method and the nuPlan dataset. In those experiments, augmentation reduced collision rates and improved safety measures relative to the reported baseline; using 10% of the original dataset with the proposed augmentation matched or exceeded the full-data baseline. Those are results from that study and benchmark, not a guarantee for vehicles operating on public roads.
Real-world human data is available for this kind of work. Honda Research Institute USA describes its HRI Driving Dataset as 104 hours of instrumented-vehicle driving collected in the San Francisco Bay Area for “learning driver behavior in real-life environments.” The dataset demonstrates that researchers can capture ordinary, messy traffic behavior at scale. It does not establish that the dataset, or any particular portion of it, is used in a production autonomous-driving system.
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How unsafe habits can enter a learned policy
Shortcut learning
A model can find an easy statistical cue instead of learning the safety reason behind an action. The Berkeley report notes that imitation-learning models can learn shortcuts from data, leading to unwanted behavior. For example, if braking demonstrations in a narrow training set frequently occur near a particular visual feature, a model might associate that feature with braking without understanding the underlying hazard. The shortcut can fail when lighting, road design, weather or traffic changes.
Distribution shift
Training and deployment rarely have identical conditions. A system trained mostly in one city, climate or road-marking standard may encounter unfamiliar layouts elsewhere. Differences can also arise between calm daytime traffic and nighttime construction, or between predictable vehicle motion and an interactive pedestrian. A policy that performs well on its training distribution can become unreliable when the distribution shifts.
Causal confusion
Correlation is not the same as cause. A vehicle may appear to succeed after following a particular trajectory because another road user yielded, not because the trajectory was inherently safe. If the model cannot distinguish the cause of success from an incidental cue, it may repeat the wrong action in a new situation.
Selection bias in demonstrations
Human drivers do not encounter every hazard equally, and a dataset can overrepresent some roads, drivers or times of day. If examples favor one driving style or omit rare but consequential events, the learned policy may be confident where it should be cautious. Selecting diverse, informative trajectories is therefore as important as collecting more miles.
Which human “bad habits” are relevant?
Human driving provides both useful expertise and unsafe behavior. The following statistics describe human crashes; they do not show that autonomous vehicles learned or reproduced these actions.
| Human behavior or statistic | What the figure means | What it does not establish |
|---|---|---|
| Distracted driving | NHTSA defines it as any activity that diverts attention from driving, including phone use, eating or drinking, passenger conversation, and adjusting stereo, entertainment or navigation systems. The agency reported 3,208 people killed in U.S. crashes involving distracted drivers in 2024 (reported on a 2025 page). | There is no finding here that a driverless system becomes distracted in the human sense or uses a phone while driving. |
| Distraction in 2021 | NHTSA reported 3,522 distraction-affected fatalities in 2021, 12% more than the 3,154 reported for 2020. | These are year-specific U.S. counts and are not a trend line for autonomous-vehicle safety. |
| Speeding, impairment and drowsiness | NHTSA’s 2021 analysis listed speeding, alcohol or drug impairment, distraction and drowsiness among risky driving behaviors. | A model can imitate an unsafe action pattern without experiencing fatigue or intoxication; the mechanism and the countermeasure are different. |
In a human dataset, an unsafe lane change, late braking or excessive speed can appear as an action to be copied. The engineering question is whether the training pipeline labels the context and filters or constrains the behavior, rather than simply matching what happened most often.
Why ordinary road tests can miss the problem
Many autonomous-driving evaluations replay fixed routes or scripted traffic. Those tests are useful for repeatability, but they can hide failures that emerge when other road users react to the vehicle.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →The HABIT benchmark creates interactive pedestrian behavior inside the CARLA simulator: pedestrians respond to the vehicle instead of following an unchanging recorded path. The Intelligent Vehicles Lab at Hochschule München University of Applied Sciences reported results for three evaluated agents that had close to or equal to zero collisions per kilometer on the CARLA Leaderboard. On HABIT, the reported maximum reached 7.43 collisions per kilometer, with a 12.94% AIS 3+ injury-risk metric; unnecessary braking reached 33% in some evaluated cases.
Those numbers are benchmark-specific outcomes in a simulated environment. They are not public-road crash rates, observed injury rates or evidence that a human driver caused a particular failure. Their importance is methodological: interactive tests can expose weaknesses that a vehicle following a fixed script never encounters. A pedestrian who pauses, steps back or moves unexpectedly can reveal whether the policy understands the interaction or merely recognizes a familiar sequence.
What evidence can—and cannot—say about deployed cars
The evidence supports a conditional claim: autonomous-driving research uses human examples, and imitation learning can acquire unwanted shortcuts or fail under distribution shift. It does not establish that deployed autonomous cars generally learn the “worst habits” of human drivers.
Several boundaries matter:
- The Berkeley result is a 2025 experiment using PLUTO and nuPlan, not an audit of commercial fleet-training pipelines.
- The HRI dataset documents research collection of real driving, not its inclusion in a production vehicle.
- HABIT results concern three agents in CARLA’s simulated, interactive benchmark; they cannot be converted into a fleet-wide collision probability.
- The human crash figures are U.S. statistics with specific years and definitions, not global measurements of autonomous-vehicle behavior.
- No reviewed source publicly demonstrates a deployed system learning a named habit such as texting, drunk driving or deliberate speeding from human examples.
That distinction prevents two opposite errors. It would be wrong to dismiss imitation-learning risk because no fleet-wide failure has been proven. It would be equally wrong to treat benchmark weaknesses or human crash counts as proof that self-driving cars behave like reckless people.
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How safer training and validation are designed
Choose demonstrations for coverage, not just volume
The Berkeley approach emphasizes informative and diverse nearby-traffic trajectories. A smaller, better-selected set can represent rare interactions and varied road conditions more effectively than a large but repetitive sample. In its reported experiments, 10% of the original dataset with augmentation matched or exceeded the full-data baseline; that result applies to the study setup, not to every dataset or vehicle.
Add explicit safety constraints
A 2021 paper by Flavia Sofia Acerbo, Mohsen Alirezaei, Herman Van der Auweraer and Tong Duy Son combines imitation learning from human highway data with barrier functions and smooth spline-based motion parameterization. The design uses a digital twin and simulation for development validation. Barrier functions can keep a planned trajectory away from defined unsafe regions, while smooth parameterization limits abrupt or physically implausible motion. This is a safety-oriented research design, not evidence that all deployed systems use those controls.
Test interactions, not only imitation scores
Behavior-matching accuracy is insufficient if the test does not challenge the policy. Evaluation should include pedestrians and vehicles that react to the autonomous car, new locations and weather, unusual but plausible maneuvers, and measures such as collision rate, injury-risk proxies and unnecessary braking. HABIT illustrates why an agent can look strong on a scripted leaderboard yet struggle when pedestrians behave interactively.
Separate development validation from public-road claims
Simulation, digital twins and benchmark datasets can reveal failure modes before deployment, but they remain abstractions. A benchmark result should be reported with its agent count, environment, metric and date. It should not be presented as a direct estimate of real-world fatalities or injuries.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhere human supervision still matters
“Autonomous” covers different levels of automation. Level 2 systems can steer and control speed within a defined operational design domain, but an attentive human driver remains responsible for supervising the system and taking over when required. They are not fully driverless.
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A 2022 study record in the U.S. National Transportation Library reports that PC-based training improved drivers’ takeover response and situational awareness when a Level 2 system reached its limits, compared with user-manual or placebo training. The finding concerns partial automation and human response, not a fully driverless car. It also shows why a system’s limits and handoff design matter: even a well-trained model may encounter conditions outside its operating domain.
A practical way to judge claims about “bad habits”
- Identify the learning setup. Ask whether the system imitates human actions, uses surrounding-vehicle trajectories, or relies on another training method.
- Check the data coverage. Look for locations, weather, road types, driver diversity and rare interactions. A single city or highway dataset cannot represent every deployment.
- Look for distribution-shift testing. Stronger evidence includes evaluation in a new location or condition, not only a random split from the same recordings.
- Ask whether pedestrians and other vehicles react. Interactive behavior is a tougher test than replaying fixed paths.
- Separate safety metrics from imitation scores. Collision, injury-risk and unnecessary-braking measures reveal failures that action similarity can hide.
- Read the qualification on every number. Confirm whether a figure is simulated, benchmark-specific, a one-time experiment, or an observed public-road rate.
- Distinguish possibility from prevalence. A documented mechanism for learning shortcuts shows what can happen; it does not show how often it happens across deployed fleets.
So, can autonomous cars learn the worst habits?
They can learn unsafe patterns if the training process rewards copying without understanding context, if the examples contain systematic mistakes, or if deployment differs from training. The risk is strongest when engineers rely on narrow demonstrations, omit interactive scenarios and evaluate only on familiar routes.
But “could” is doing important work in the headline. Current evidence does not show that autonomous cars generally reproduce human distraction, impairment, drowsiness or speeding. Safer data selection, explicit motion constraints, interactive simulation and careful operational limits are designed to prevent a model from treating every observed human action as a rule to follow.
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