How to Approach Prototyping Perception Systems for SAE Level 2 Automation Assignments

Dr. Aaron Mitchell

United States

Matlab

Dr. Aaron Mitchell has over 12 years of experience in automotive automation and control systems. He earned his Ph.D. in Electrical Engineering from Southern Illinois University, USA.

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In a typical assignment, you might be tasked with designing subsystems for a vehicle equipped with ACC. This involves:

• Sensor hardware modeling – Cameras, radars, and lidars that detect vehicles, pedestrians, or road signs.
• Algorithm design – Implementing adaptive cruise control logic, object detection, and decision-making systems.
• System-level interaction – Understanding how sensors interact with controllers and actuators for real-time vehicle control.

By creating and testing these models in MATLAB, you can quickly iterate design choices, identify errors, and refine your approach before applying it to real-world testing.

Visualizing and Simulating Sensors for Maximum Detection

One of the first steps in building a perception system is selecting and configuring sensors. In assignments, you will often work with virtual sensors using the Driving Scenario Designer app in MATLAB. This tool lets you:

• Model sensors based on specification sheets.
• Visualize coverage areas using Bird’s-Eye Plots (BEP).
• Test detection limits and blind spots in different scenarios.

For example, vision-based sensors like Mobileye systems can identify vehicles, pedestrians, lane markings, and road signs within a defined field of view (FOV). Radar sensors, such as mid-range radars, extend detection distances and add robustness in conditions where cameras might fail, such as fog or low light.

By simulating different sensor placements and coverage areas, students can assess how well a perception system detects objects in various driving scenarios. MATLAB scripts can also be used to automatically generate BEPs and evaluate sensor layouts.

Testing Sensor Layouts in Virtual Environments

After defining the types of sensors, the next step is experimenting with their placement on the vehicle model. Assignments may require you to:

1. Place sensors at different locations (e.g., front fascia, windshield, rear corners).
2. Create road networks with varying conditions such as turns, hills, and intersections.
3. Insert vehicles, motorcycles, or pedestrians into the environment to test coverage.

For example, placing radars at the corners of the vehicle provides wider side coverage, while a forward-facing camera provides lane detection. However, blind spots may still exist—such as directly behind the car or at certain lateral angles.

By simulating these configurations, you can compute detection accuracy, error percentages, and determine which sensor layout minimizes blind spots while maintaining cost-effectiveness.

Understanding Limitations of Perception Systems

Every sensor has limitations, and acknowledging them is crucial in your assignments. For instance:

• Cameras: Limited range in poor lighting or adverse weather.
• Radar: Limited lateral resolution; may misclassify objects.
• Blind spots: Areas where no sensor can reliably detect obstacles.

Assignments may require you to identify such blind spots and propose solutions. Sensor fusion, discussed later, is one of the most effective methods to address these challenges.

For example, while a small car may always be detectable within the sensor’s FOV, motorcycles present a more complex challenge as they can occupy less visual space and remain undetected in blind spots. These scenarios highlight the importance of integrating multiple sensors.

Implementing Sensor Fusion for Reliable Detection

Sensor fusion is at the heart of modern perception systems. It involves combining data from multiple sensors—radar, lidar, cameras—into a single, reliable picture of the environment.

In MATLAB and Simulink, students can implement sensor fusion algorithms such as:

• Kalman filters – For object tracking with noisy data.
• Global Nearest Neighbor (GNN) algorithms – For associating detected objects across time.
• Multi-sensor data processing pipelines – For tracking objects across different blind spots.

A classic assignment exercise involves simulating a motorcycle moving into a blind spot. With a single sensor, the motorcycle may disappear from detection, but with sensor fusion, the fused data ensures that the object continues to be tracked.

This approach builds robustness and reliability into the automated system and provides a strong basis for assignments focusing on real-world system limitations.

Simulation of Real-World Scenarios

Assignments often emphasize the ability to replicate real-world driving conditions through simulation. Some scenarios you may need to build include:

• Vehicles entering lanes suddenly.
• Banked or blind turns.
• Overpasses that might be misclassified as obstacles.
• Multiple objects in close proximity.

Using the Driving Scenario Designer in MATLAB, you can introduce these challenges and test how your designed perception system responds. For example, you can simulate a vehicle cutting into your lane unexpectedly and assess how quickly and accurately the sensors detect and respond.

Validating Simulations with Physical Testing

While simulation provides a safe environment for experimentation, validation with real-world data is essential. In practice, this is done with mule vehicles—test cars equipped with prototype sensors. For assignments, students are often asked to discuss or analyze how simulation results compare to real-world sensor data.

Typical validation steps include:

1. Mounting sensors – Cameras on windshields, radars on front and rear fascia, etc.
2. Logging data – Using CANoe or other data acquisition systems to store detections.
3. Exporting data – Saving logs in .mat format for MATLAB analysis.
4. Visualizing results – Using functions like plotDetection to compare raw detections with ground-truth video footage.

During validation, discrepancies often appear—for example, radars may detect stationary objects incorrectly, or cameras may pick up false positives. These imperfections are expected and help refine the algorithms.

Assignments on Data Analysis and Sensor Fusion

One of the most engaging parts of MATLAB-based assignments is analyzing logged data. You may be asked to:

• Filter noisy data using Kalman filters.
• Compare raw sensor detections with fused outputs.
• Plot tracking histories of vehicles across multiple scenarios.

For instance, by applying sensor fusion to raw radar data, you can demonstrate how filtered results track a vehicle smoothly, even when the raw input contains significant noise or dropouts.

Such exercises help you build confidence in both your MATLAB programming skills and your understanding of autonomous vehicle dynamics.

Future Directions in Perception System Prototyping

Assignments often conclude with forward-looking tasks. For SAE Level 2 systems, future work includes:

• Expanding sensor networks – Adding more radars or cameras to cover blind spots.
• Improving fusion algorithms – Using advanced probabilistic approaches for multi-object tracking.
• Deploying algorithms on hardware – Running MATLAB/Simulink code on embedded processors or AI platforms.
• Integrating with control systems – Linking perception data with Adaptive Cruise Control (ACC) or lane-keeping controllers.

Students may also be tasked with simulating deployment pipelines, where data is processed through a ROS node or AI edge computing platform. MATLAB’s Robotics System Toolbox plays a vital role here, enabling integration with Robot Operating System (ROS) for real-time data handling.

Conclusion

Prototyping perception systems for SAE Level 2 automation assignments requires a strong balance between simulation and theory. Using MATLAB and Simulink, you can design, test, and refine models for sensor layouts, fusion algorithms, and real-world driving scenarios. By understanding the limitations of individual sensors and applying data fusion strategies, reliable automated driving systems can be developed.

For students, these assignments provide an excellent opportunity to combine programming, system modeling, and data analysis into practical, real-world applications. Whether you are building ACC controllers, testing blind spot detection, or fusing radar and camera data, MATLAB provides the perfect environment to turn theory into practice.

As automation technology evolves, students mastering these assignments today will be at the forefront of tomorrow’s intelligent transportation systems.