## 5 Comparative Analysis and Future Outlook
### Algorithmic Differences and Development Trajectories  
The algorithmic approaches used in ADASs and FSD systems differ significantly, reflecting their distinct objectives and operational domains. This section explores these differences and the potential for integration of these technologies.  
FSD systems require more complex decision-making processes compared to ADASs. [PERSON] et al. (2012) analyzed the decision-making architectures in autonomous vehicles, highlighting the shift from rule-based system learning as a common standard in ADASs to more sophisticated machine learning approaches in FSD systems. The environmental understanding capabilities of FSD systems are generally more comprehensive than those of ADASs. [PERSON] et al. (2013) compared the perception systems used in ADASs and FSD systems, noting that FSD systems typically employ more advanced sensor fusion techniques and have a broader spatial and temporal understanding of the driving environment.  
One of the key challenges in developing FSD systems is handling edge cases and unpredictable scenarios. [PERSON] (2014) discussed the concept of \"operational design domain\" (ODD) and its implications for autonomous driving, emphasizing the need for FSD systems to handle a more comprehensive range of scenarios compared to ADASs.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Aspect** & **Adverse Weather Conditions** & **Complex Traffic Scenarios** & **Emergency Maneuvers** & **Blind Spot Management** \\ \hline Perception Technologies & Fusion of LiDAR and radar systems & High-resolution cameras & LiDAR (range: 200 m) & Short-range radar (30 m) \\ Secondary Sensor & Infrared cameras & LiDAR for 3D mapping GPS/IMU for localization Spatio-temporal fusion & Stereo cameras (150 m) & Wide-angle cameras (50 m) \\ \hline Algorithmic Approaches & & & & \\ \hline Main Algorithm & DeepWet-Net for rain removal & Social-LSTM for trajectory prediction Intention-aware motion planning & Model predictive control & YOLOv5 for object detection \\ Auxiliary Algorithms & Fog density estimation & & & Reinforcement learning for making decision-making & Graph Neural Networks for spatial reasoning \\ Accuracy (Standard/Adverse) & 92\%/78\% & 95\%/85\% & 94\%/85\% & 97\%/90\% \\ Computational Complexity & O(n\({}^{\circ}\)2) for image dehazing [20] & O(nlogn) for multi-object tracking & O(n\({}^{\circ}\)2) for MPC & O(n) for single-stage detection \\ \hline Testing and Validation & CARLA with weather modules & SUMO for urban traffic Urban and highway vehicles & PreScan for ADAS testing & SynCity for diverse scenarios \\ Real-World Testing & Dedicated bad weather tracks & Urban and highway environments & Closed courses with obstacles & Specialized blind spot test tracks \\ Key Performance Metrics & Weather condition classification accuracy & Prediction accuracy of road user behavior & Collision avoidance success rate & False positive/negative rates \\ Benchmark Datasets & BDD100K Weather & Waymo Open Dataset & EuroNCAP AEB scenarios & scenes \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(Cont.\)There is growing interest in integrating ADASs and FSD technologies to create more robust and versatile systems. [PERSON] et al. (2015) proposed a hierarchical framework that combines the reliability of ADASs with the advanced capabilities of FSD systems, potentially offering a pathway to safer and more capable autonomous vehicles.