The road to autonomy: It is all about machine learning on today’s roads
By Pam Oakes, MACS Senior Technical Trainer and Curriculum Developer – Published on 8/26/2025
Advanced Driver Assistance Systems (ADAS) have undergone a remarkable transformation in the past 10 years. This evolution is driven by merging sensor-fusion hardware, data grapevines, and increasingly capable machine learning (ML) models.
As early as the late 1980s, ADAS iterations were utilitarian: ultrasonic sensors paired with simple microcontrollers offered rudimentary reverse parking alerts to the compass on the rearview mirror. These systems were reactive, low-speed, and single-purpose. See Figure 1.
As automotive platforms matured, RADAR and mono-vision systems entered the mix, enabling functions like forward collision warning to adaptive cruise control (Figure 2). Sensor fusion — the coordinated processing of radar and visual data — significantly improved system robustness in challenging conditions.
Today, with the advent of “deep learning” it is creating a shift from deterministic logic to data-driven perception at the Tier 1 level to aftermarket companies’ vision to incorporate into those vehicles without ADAS conveniences. Then came convolutional neural networks (CNN) to centralize tasks. A good example is lane detection, object tracking, and pedestrian recognition. With the introduction of GPU (graphics processor unit) acceleration and purpose-built SoCs (systems on chips) supporting real-time instruction within the vehicle.
Contemporary ADAS stacks – like RADAR, LiDAR, proximity sensors, and cameras – these systems enable heavy-Level 2 and light-Level 3 autonomy in production vehicles, today. Seen on platforms like Tesla’s Autopilot, General Motors’ Super Cruise, Ford’s BlueCruise, and Mercedes-Benz’s Drive Pilot (Figure 3).
Equally important is the shift in architectural philosophy. Modern ADAS is built on principles of functional safety (e.g., ISO 26262), with redundancy and fail-operational design to maintain capability in the event of component failure. Over-the-air (OTA) updates further extend system longevity, allowing for continuous algorithmic refinement post-deployment, and data-gathering for duel-twin learning.
ADAS has come a long way from the “beep-beep-beep” of the backup sensor communication with the driver.
About the author: Pam Oakes is a Senior Technical Trainer and Curriculum Developer at MACS. Pam provides automotive training at all levels, including train-the-trainer, professional technicians, and scholastic programs, with over 20 years of hands-on experience running a 12-bay shop in Florida. She has been a MACS Section 609 proctor since 2016 with additional expertise including ADAS calibration, fleet training, and technical curriculum development for major corporations. You can reach Pam at poakes@macsmobileairclimate.org.
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MACS Action Magazine – JULY/AUG 2025 Issue
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