This is like giving a car an extra pair of smart eyes and a fast brain so it can see the road, recognize dangers (cars, pedestrians, lanes, signs), and react quickly and safely. The paper reviews how camera-based vision and mathematical optimization are combined to make these assistance features more accurate and reliable.
Manual driving is prone to human error—slow reaction times, missed hazards, and fatigue. Camera-only ADAS must work in real time and in all conditions while running on limited in-vehicle hardware. The reviewed techniques aim to improve detection accuracy and robustness (for lanes, vehicles, pedestrians, traffic signs) and optimize algorithms so they run fast and reliably enough for real-time driver assistance and accident prevention.
Domain-validated perception and control algorithms tuned to real-world driving data, plus optimization tricks that let them run reliably on low-power automotive hardware. Over time, the moat comes from proprietary training data, algorithm refinements, and system integration expertise rather than any single model.
Classical-ML (Scikit/XGBoost)
Unknown
High (Custom Models/Infra)
On-vehicle compute limits and latency for real-time, high-resolution video processing under diverse environmental conditions (night, rain, occlusion), plus dataset coverage for edge cases.
Early Majority
Focus on camera-based ADAS algorithms tightly coupled with optimization techniques (for speed, robustness, and resource usage) rather than purely sensor-fusion or end-to-end deep-learning approaches. This bridges classical computer vision, optimization, and embedded implementation for automotive-grade performance.