Most autonomous driving systems rely on supervised learning. In automotive development, AI teams create perception models by annotating multi-sensor datasets from LiDAR, radar, and cameras. Success depends on integrating scalable, cost-efficient human feedback into data pipelines—enabling teams to train and validate autonomous systems that are safe, reliable, and aligned with human expectations.
Proprietary data has become a critical differentiator for Advanced Driver Assistance & Autonomous Driving Systems (ADAS/AD). With all competitors accessing similar algorithms, product success depends on the quality of datasets—and specifically, on how efficiently teams can turn scarce human judgment into high-quality, annotated sensor-fusion data.
To unlock greater efficiencies and improve model performance and safety, auto manufacturers need to produce and manage datasets composed of complex objects and sequences. The rapidly growing field of sensor hardware requires sophisticated management of multi-modal "sensor-fusion" data.
Dataset quality is crucial for automotive product success, but models are only as good as the data they're trained on. Achieving high-quality datasets isn't a one-time task—it requires an iterative process. Just as developers wouldn't write 20 million lines of code all at once, datasets that power ADAS/AD products need continuous development and refinement. Unfortunately, many auto manufacturers still view data as a fixed asset, resulting in suboptimal performance and safety.
High-quality data directly impacts model performance, generalization, bias, robustness, and overall efficiency in real-world ADAS/AD applications. To improve autonomous vehicle safety, teams need cost-efficient processes that integrate human feedback: What objects affect performance? Cars? Pedestrians? Reflections? Stationary objects? Addressing these questions efficiently is how automotive manufacturers maximize their annotation budgets and train ML models with sensor-fusion data.
Once datasets are deployed into training, feedback loops between dataset assessment and model performance drive an iterative process that's invaluable to ADAS/AD product success. Tools that reveal anomalies, improve data quality, and add new data where needed enhance model capabilities. An iterative approach also helps prevent model bias, as data augmentation techniques help models differentiate between objects more accurately.
Consider pedestrians on billboards. While obvious to human observers that these aren't real pedestrians, it may not be clear to the machine. Teams must determine how to handle this ambiguity. One solution: don't annotate those objects and let the model detect them as pedestrians. Another: add annotations to your dataset to avoid punishing your model for detecting them. Either way, these situations demand an iterative cycle on your dataset.
When it comes to dataset size, increasing volume only where it has positive impact—not adding more data you already have—offers diverse examples for better model training. Many OEMs and Tier 1/Tier 2 manufacturers capture kilometer after kilometer of highway data, but when this coverage doesn't contain rare occurrences that improve the ML model, greater size doesn't equate to better results. The goal is to get the most annotated data for your budget.
Here are three tips for AI product teams that want to improve ML model success by implementing iterative datasets during development, production and beyond:
Gaining an iterative understanding of machine learning datasets is key to success in AI, especially in ADAS/AD products. By focusing on dataset quality through continuous improvement, organizations that invest in cost-efficient, iterative human feedback—integrating scalable annotation, curation, and verification processes—will deliver the most annotated autonomy data for their budget and win the race to deploy impactful results in diverse real-world scenarios.