The Software Brain — How Autonomous Vehicles Plan and Decide

Perception tells a vehicle what’s around it. Control systems move the vehicle based on commands. But the critical layer that bridges both is planning and decision-making. This is the "software brain" – where raw data becomes intention, and intention becomes action.

In this blog, we demystify the core software architecture of autonomous driving: from behavior prediction and path planning to real-time execution. And we show why autonomy isn’t about intelligence – it’s about safety, certainty, and speed in decision-making.

From Perception to Action: The Planning Pipeline

Autonomous vehicles follow a structured flow:

1. Perception: The world is detected and classified (vehicles, people, objects, road markings)
2. Localization: The vehicle's exact position is established using GPS, IMUs, and HD maps
3. Prediction: Other agents’ future behavior is estimated (e.g. pedestrian paths, oncoming traffic)
4. Path Planning: A safe and efficient path is calculated based on the vehicle’s goal and environment
5. Motion Control: Commands are executed via Drive-by-Wire systems

This planning loop repeats every 10–50 milliseconds, depending on the system. That means decisions must be made faster than a human can blink.

“Driving is not just about sensing – it’s about reacting. In autonomy, the delay between input and action can be the difference between safety and disaster.”
- Prof. J. Christian Gerdes, Stanford University Center for Automotive Research

Inside the Stack: Key Software Modules

The autonomy stack includes several core software layers:

• Mapping & Localization: HD maps (cm-level accuracy), SLAM algorithms
• Object Tracking & Prediction: Kalman filters, neural networks, reinforcement learning
• Path Planning: Hybrid A*, RRT*, dynamic programming, behavior-based planners
• Decision-Making: Rule-based logic + machine learning
• Fallback Strategies: Emergency stop, rerouting, or requesting teleoperation support

Industry leaders like Mobileye, NVIDIA, and Oxbotica integrate these into modular stacks, often tailored to vehicle type, use case, and regulatory requirements.

Fail-Operational Logic: Because Not Deciding Is Also a Decision

In safety-critical environments – ports, defense, mining – inaction or indecision can be lethal. That’s why fail-operational decision-making is core to AD stack design:

• Predefined “fallback behavior” in case of sensor loss or system failure
• Rule-based “minimal risk conditions” (e.g., gradual stop on road shoulder)
• Seamless handover to teleoperation, if applicable
• Black box logging for post-event analysis and certification compliance

This is reinforced by safety standards such as ISO 26262, ISO 61508, UNECE R155, and UL 4600 for autonomous systems.

Human-In-The-Loop or Machine Only? Sector-Specific Realities

Autonomy isn't one-size-fits-all. Different use cases require different levels of control, oversight, and decision authority:

• Public Transport: Often geo-fenced, with safety drivers or remote supervisors
• Logistics: Mix of predefined routes + AI-based dynamic rerouting
• Defense: Requires embedded ethical rules (e.g., no weaponized AI autonomy)
• Mining/Agriculture: High predictability allows for full autonomy in controlled zones

Arnold NextG systems interface with these decision layers by executing commands with certified safety and providing real-time status feedback for higher-level planning modules.

The Road to Certification: Why Planning Must Be Auditable

Autonomous decision-making must be explainable, testable, and certifiable. Regulators increasingly demand:

• Model transparency: Why did the vehicle make this maneuver?
• Scenario coverage: Has it been tested in this exact situation?
• Failproof logic: Can it recover from corner cases?

That's why simulation, scenario libraries (e.g., PEGASUS, ASAM OpenSCENARIO), and real-world data validation are now standard in autonomous development and pre-deployment.

Conclusion: Fast, Safe, Transparent Decisions – or No Autonomy at All

Autonomy isn’t just a function of intelligence—it’s a function of trust. If machines are to take over responsibility from humans, their decisions must be predictable, defensible, and certifiable – under every circumstance.

In our next article, we’ll explore how functional safety, cybersecurity, and standards build that trust across the entire autonomous ecosystem.

References

• ISO 26262, ISO 61508, UNECE R155, UL 4600: Functional safety and cybersecurity in autonomous systems
• Prof. Christian Gerdes, Stanford CARS lecture series, 2022
• Mobileye vs. Arnold NextG Safety Architecture Comparison, 2025
• BMDV (2024), Handbuch Autonomes Fahren – Öffentlicher Verkehr

• PEGASUS Project & ASAM Standards, 2023