Engineering Intelligence Under Uncertainty: Real-Time ML for Autonomous Systems

It is easy to assume that engineering mastery is forged in code. But in the world of autonomous systems, it is often forged in how systems respond to ambiguity, manage uncertainty, and interpret human behavior under constraint.

In the evolving field of artificial intelligence and robotics, the ability to embed human-like interpretation into machine decision-making has become increasingly essential. As AI systems move from executing predefined logic to navigating complex, real-time environments, engineers must design models that reason, adapt, and respond in situ.

For Neha Boloor, a senior IEEE member and a machine learning engineer at Zoox, building autonomous systems goes beyond precision tuning. At the intersection of generative AI and robotic decision systems, she focuses on safety-critical ML architectures that help intelligent agents perceive and act reliably. Designing these systems involves more than algorithmic efficiency, it demands architectural awareness and the ability to manage unpredictability in real-time.

This is not abstract. In behavior modeling, attention dynamics, and inference latency, Neha’s ability to surface modeling blind spots often comes down to recognizing when outputs fail to reflect context, when a pedestrian might hesitate, when a trajectory diverges from expectation, or when the system’s reaction lacks real-world feasibility.

“In the lab, we optimize. In the real world, we interpret,” she says. “That’s where intuition matters.”

Modeling Under Temporal Constraints

Behavior prediction models are particularly sensitive to ambiguity. In real-world robotic systems, even a small misjudgment, such as whether a cyclist will brake or maintain speed, can trigger planning errors downstream. The core issue is often not prediction error, but temporal misalignment.

“Sometimes, the model doesn’t fail because it’s inaccurate,” Neha, a Gold Winner at Globee® Awards for Achievement, explains. “It fails because it’s out of sync.”

She refers to decision latency: the delay between environmental perception and action execution. Many ML models, particularly those trained on static trajectory maps or simplified motion cues, generate predictions that arrive either too early or too late to influence the planning stack effectively.

To mitigate this, Neha has focused on improving how behavioral timing is incorporated into model feedback. By aligning inference cadence with naturalistic delays, such as deceleration or reactive glance cues, her systems have achieved better responsiveness in multi-agent scenarios, where timing determines safety margins.

This is not a surface-level adjustment. It is architectural refinement that improves how models behave under real-world, event-driven conditions.

Feature Hierarchies in Perception Failures

Pattern recognition is a baseline for both human and machine interpretation, but in autonomous systems, the structure of that recognition must be intentionally prioritized. Neha encountered a recurring failure case where the system consistently misclassified construction scaffolding as a pedestrian barrier. Upon deeper inspection of point cloud data and mesh overlays, she identified the problem: the model was over-relying on vertical symmetry while underweighting depth variance.

This was not a failure of the classifier itself, it was a misalignment in the perceptual hierarchy within the network architecture.

Neha’s solution was to restructure the evaluation pipeline using salience prioritization and revised feature weighting across occluded inputs. By shifting how the model assessed spatial context, especially in synthetic renderings and edge-case training data, she improved both recall and the system’s robustness in cluttered, urban environments.

“Models need to generalize from partial input,” she says. “Our job is to make sure they infer structure, not just shape.”

Engineering for Constraint-Driven Debugging

Machine learning models in robotics rarely fail in isolation. They fail as part of larger pipelines, with failures propagating due to tight coupling between modules, incomplete sensor inputs, or computational ceilings.

Neha’s experience spans a range of system constraints, from startup environments reliant on vision-only pipelines to enterprise-grade behavior stacks. In both cases, the common thread is adaptive debugging: engineering for failure detection in real-time and building models that tolerate deviation.

Her approach emphasizes flexibility over rigid abstraction. In one example, a model trained on pedestrian-agent interactions performed as expected during structured evaluation, yet failed in deployment when two agents crossed paths asynchronously. Rather than retraining the model with more data, Neha redefined the temporal interaction window, tuning the system to more accurately detect the onset of meaningful agent interaction.

These fixes do not emerge from predefined checklists. They result from system thinking, identifying how and where timing, architecture, and interface layers introduce fragility.

Real-Time Interpretation as a Safety Imperative

As autonomous systems progress from pilot to deployment phases, the expectation is not only performance, but resilience. Systems must account for unmodeled behavior, incomplete signals, and edge-case inputs without compromising operational safety.

This is where Neha’s engineering philosophy plays a critical role. Her models are designed not only to react, but to interpret, adjusting inference dynamically based on contextual weighting, sensor limitations, and behavior prediction reliability. “Some problems are not solved by adding complexity,” Neha reflects, drawing from insights developed in her scholarly paper Strategic Investment in CX Infrastructure: A Capital Deployment Framework for Growth-Stage Tech Companies. The work outlines how engineering decisions—whether in capital infrastructure or autonomous systems—must be shaped by long-term adaptability under dynamic constraints. “They are solved by engineering models to adapt under pressure.”

For ML systems to support real-world autonomy, they must operate with both precision and slack, making decisions fast enough to act, and informed enough to know when not to. In Neha’s view, engineering intelligence is not just about solving the known, it is about preparing for the uncertain.

Because in real-world autonomy, performance is not defined by perfection. It is defined by how well the system holds when conditions deviate.