Have you encountered this situation? When an autonomous driving system fails to navigate a construction zone properly, adding relevant data and adjusting the model improves performance at that specific location. However, when the vehicle encounters another construction site with different temporary lane markings, road widths, and surrounding vehicle behaviors, the system may still exhibit unreasonable deceleration, hesitation, or improper detours.
As training data accumulates and models iterate, why do some driving issues persist? This problem is not necessarily due to insufficient data volume. For autonomous driving systems, capturing a specific scenario does not mean the training data has fully covered related issues; achieving better results on existing data does not mean the model has learned to handle similar scenarios stably under varying conditions.
01. Why Are Complex Scenarios Still Handled Poorly Despite More Data?
Driving data from real roads exhibits significant distribution differences. In training datasets, routine scenarios like car-following and lane-keeping often occupy a large proportion, while coverage of scenarios such as construction rerouting, sudden crossing by non-motorized vehicles, and multi-vehicle interactions depends on collection routes, data filtering, and dataset composition. These scenarios involve various changes in road structures, traffic participant behaviors, and their combinations. If new data primarily comes from common driving scenarios, although the training set size expands, the model's experience in critical scenarios may not increase synchronously.
Even if the data contains numerous construction zones, if the road width, marking status, and vehicle interaction patterns are highly similar, it is difficult to cover the various variations found on real roads. More complex problems arise when multiple factors occur simultaneously.
For example, a construction zone may have both original lane lines and temporary markings/cones. The system needs to combine road structure, obstacle positions, and the motion states of surrounding vehicles to determine the currently passable area and plan an appropriate trajectory. If an adjacent vehicle suddenly cuts in, the originally feasible path may need re-evaluation.
The fact that the model has seen cones does not guarantee it correctly understands the construction area; seeing a vehicle cut-in does not guarantee it can handle simultaneous construction rerouting and vehicle cut-ins.
To evaluate the value of training data, one cannot simply look at cumulative mileage, collection duration, or sample count. It is necessary to analyze scenario distribution, combinations of key factors, and whether different driving behaviors are adequately covered. Whether new data fills the model's capability gaps is more important than merely increasing quantity.
02. Why Does Collecting Failure Data Not Always Solve Problems?
When an autonomous driving system encounters issues, data must be traced back to identify where the problem occurred before deciding how to improve training. Taking unreasonable deceleration in construction zones as an example, the issue might stem from inaccurate identification of temporary road boundaries, or the system might have identified the passable area but failed to sufficiently consider the motion trends of adjacent vehicles.
In another case, the system may generate a feasible trajectory, but the speed changes or steering angles within that trajectory are not reasonable. While these problems all manifest superficially as unreasonable deceleration, the underlying causes may be entirely different. If the key factors affecting driving behavior are not identified, and only similar driving records are continuously added, the model may not improve effectively. Data quality and organization also impact training results.
For multi-sensor training schemes, accurate temporal synchronization and spatial alignment of data are crucial. When using supervised learning, the reliability of labels or reference trajectories is equally important. Additionally, the proportion of hard samples in the training data, sampling methods, and training objectives affect whether the model can learn effective patterns from these samples.
For imitation learning relying on demonstration data, the model generally learns from reference driving behaviors. If demonstrations are inconsistent, or if multiple reasonable driving choices exist for similar scenarios, the training process must properly handle these discrepancies. Learning to reproduce behaviors in training samples does not mean the model has mastered safe decision-making rules for all scenarios.
Furthermore, not all problems can be solved by adding data. If the issue stems from model architecture, training objectives, planning constraints, or vehicle execution capabilities, simply increasing sample numbers may not address the root cause. The value of failure data lies in helping R&D personnel locate problems and provide targeted improvement basis for subsequent training.
03. Why Do Issues Reappear in Similar Scenarios After Being Solved?
Even if the training process improves certain driving behaviors, the model still faces the challenge of transferring from known scenarios to new ones. For instance, if the model successfully navigates one construction zone, its previous driving strategy may not apply when encountering another zone with narrower roads, blurrier temporary markings, and denser adjacent traffic. The model needs to make judgments based on the current environment rather than simply reproducing previously learned behaviors. This involves the model's generalization capability. Since training data cannot exhaust all road conditions, the model must learn rules from limited samples that apply to different scenarios.
Another easily overlooked issue is that the states encountered during actual driving may gradually deviate from common states in the training data. In imitation learning, for example, the model learns from reference driving behaviors and corresponding environmental data during training. During actual operation, the actions or trajectories output by the model influence the vehicle's subsequent motion state. Once the vehicle deviates from the expected trajectory, subsequent inputs and traffic interaction conditions may change, causing the states faced by the model to gradually deviate from the common distribution in the training data.
If a vehicle chooses an inappropriate trajectory in a construction zone, it may approach cones too closely, compressing the space for subsequent adjustments. At this point, the environment facing the vehicle has changed, potentially affecting subsequent decisions. If the model lacks experience handling these deviated states, early judgment errors may amplify progressively through continuous decision-making.
The discrepancy between state distributions encountered during training versus actual operation is a key problem that imitation learning and closed-loop driving must address. However, specific mechanisms vary across different autonomous driving architectures, so repeated issues should not be attributed to a single cause. Determining whether a model has truly solved a problem requires verification at multiple levels.
Offline replay checks the model's processing results on existing data; closed-loop simulation observes the impact of model decisions on subsequent states and traffic interactions, though results are limited by simulation realism; road testing further verifies performance in real environments, constrained by test routes, traffic conditions, and scenario coverage. These methods complement each other to comprehensively judge whether model improvements are effective.
04. What Should Autonomous Driving Training Truly Focus On?
When a class of driving issues recurs repeatedly, the task is not simply to judge how much data is missing, but to investigate what problems exist between data coverage, training mechanisms, and actual model performance.
If critical scenarios are under-covered, targeted data supplementation is needed; if sample quality or labeling has issues, data processing must be improved; if the model fails to learn effective driving rules, training objectives and methods should be reviewed; if offline results improve but closed-loop performance does not, further analysis of continuous decision-making and traffic interactions is required.
After each optimization, it is necessary to check if the original issue has improved and if new problems have emerged in similar or related scenarios. Achieving better results on a single fixed case is insufficient to prove the model has stable capability in handling similar scenarios. It is also necessary to observe performance on other key driving tasks to avoid local optimizations causing new performance regressions.
For autonomous driving R&D, training data scale remains an important foundation, but data volume is not directly equivalent to driving capability. Data distribution influences which situations the model has encountered and the role different scenarios play in training; the training process influences what the model learns; and closed-loop tests and road validation help R&D personnel determine if these capabilities function in actual driving.
What autonomous driving truly needs is not just continuously increasing training data, but ensuring every problem discovery drives effective improvement, confirmed through comprehensive validation. Only when the model performs stably across representative similar and related scenarios does data accumulation truly translate into driving capability.