<p>Artificial intelligence has fundamentally transformed object detection in Advanced Driver Assistance Systems (ADAS), enabling robust environmental perception across diverse and challenging driving conditions. Through a systematic synthesis of 236 peer-reviewed studies published between 2020 and 2025, this review identifies three key findings: transformer-based architectures, particularly BEVFusion and RT-DETR—consistently outperform CNN-based detectors in 3D detection accuracy on benchmark datasets (nuScenes, KITTI, Waymo), but at the cost of 2–4 × higher computational latency that remains prohibitive for embedded automotive platforms, multimodal sensor fusion integrating camera, radar, and LiDAR data yields the highest robustness under adverse weather and low-light conditions, though the transition from late fusion to feature-level fusion introduces unresolved synchronization and calibration challenges, and Generative AI—including diffusion models and world models—shows strong promise for synthetic data augmentation to address the annotation bottleneck, yet standardized evaluation protocols for synthetically augmented ADAS training sets remain absent. Critical research gaps include the lack of unified benchmarks for safety–critical perception, insufficient XAI integration in production ADAS, and the computational barrier to real-time deployment of state-of-the-art fusion networks. This review provides a structured taxonomy, a dataset-aligned comparative analysis, and a five-year technical roadmap to guide future research in scalable, interpretable, and safety-compliant ADAS perception systems.</p>