# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 4 Comparison of Selected AI Models
### Results on the CARLA Platform  
CARLA Leaderboard 1.0 is an earlier version of the platform for assessing autonomous driving algorithms in the CARLA simulator, characterized by photorealistic graphics (Figure 4). Algorithms are evaluated based on simpler scenarios. In turn, CARLA Leaderboard 2.0 is the latest version, introducing more complex scenarios, such as door-opening maneuvers or giving way to emergency vehicles. Leaderboard 2.0 also supports more sensors, including eight RGB cameras, two LIDAR scanners, and four radars. Figure 4: Examples of images from the CARLA simulator: (**a**) street view and (**b**) view from the car’s perspective. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **NDS** & **mAP** & **mATE** & **mASE** & **mAOE** & **mAVE** & **mAAE** & **Year** & **Ref.** \\ \hline EA-LSS & 0.78 & 0.77 & 0.23 & 0.21 & 0.28 & 0.20 & 0.12 & 2023 & [146] \\ BEVFusion-e & 0.76 & 0.75 & 0.24 & 0.23 & 0.32 & 0.22 & 0.13 & 2022 & [147] \\ FocalFormer3D-F & 0.75 & 0.72 & 0.25 & 0.24 & 0.33 & 0.23 & 0.13 & 2023 & [148] \\ UniTR & 0.75 & 0.71 & 0.24 & 0.23 & 0.26 & 0.24 & 0.13 & 2023 & [149] \\ FocalFormer3D-TTA & 0.74 & 0.71 & 0.24 & 0.24 & 0.32 & 0.20 & 0.13 & 2023 & [148] \\ MEFormer & 0.74 & 0.72 & 0.27 & 0.24 & 0.30 & 0.27 & 0.11 & 2024 & [150] \\
3D Dual-Fusion\_T & 0.73 & 0.71 & 0.26 & 0.24 & 0.33 & 0.27 & 0.13 & 2022 & [151] \\ FocalFormer3D-L & 0.73 & 0.69 & 0.25 & 0.24 & 0.34 & 0.22 & 0.13 & 2023 & [148] \\ MGTANet & 0.73 & 0.67 & 0.25 & 0.23 & 0.31 & 0.19 & 0.12 & 2022 & [152] \\ CenterPoint & 0.71 & 0.67 & 0.25 & 0.24 & 0.35 & 0.25 & 0.14 & 2020 & [153] \\ SSN & 0.62 & 0.51 & 0.34 & 0.24 & 0.43 & 0.27 & 0.09 & 2020 & [154] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of selected models capable of detecting 3D objects tested on the nuScenes video set. Tables 6 and 7 present the performances of the leading deep neural models on the CARLA Leaderboard tasks 1.0 and 2.0, respectively, with metrics including Driving Score, Route Completion, and Infraction Penalty/Score. ReasonNet achieves the highest Driving Score (79.95) and Route Completion (89.89), indicating a superior overall performance. ReasonNet's end-to-end framework integrates temporal and global reasoning to capture dynamic environmental changes and comprehensively understand scene contexts. LAV demonstrates the highest Route Completion score (94.46) and lowest Infraction Penalty (0.64), highlighting its efficiency in route completion and safety. LAV's approach aggregates data from multiple vehicles to develop robust models capable of handling diverse driving scenarios. Interpretable Sensor Fusion Transformer integrates data from multiple sensors, offering an improved interpretability and analysis of the fused information. Trajectory-guided Control Prediction (TCP) utilizes predicted trajectories for control decisions, effectively bridging perception and action planning. Hidden Biases of End-to-End Driving Models examines the biases in autonomous driving systems, revealing how the training data and model architecture may influence decision making. TransFuser Models employ transformer-based frameworks for sensor fusion, enhancing decision making through the integration of multi-modal sensor data. In Table 7, CarLLaVA achieves the highest Driving Score (6.87) and a commendable Route Completion score (18.08), with a low Infraction Score (0.42). It leverages a Vision Language Model for camera-only autonomous driving, integrating visual and linguistic data for advanced decision making. CarLLaVA (Map Track) excels with the highest Route Completion score (18.89) and lowest Infraction Score (0.39), making it exceptionally robust in route execution and safety. The TF++ model focuses on uncovering and mitigating biases within autonomous driving systems by integrating bias detection mechanisms with transformer architectures, ensuring more reliable and equitable driving decisions across varied conditions.