# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Path and Motion Planning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[161] & CNN & \begin{tabular}{c} Obtained 100\% autonomy \\ on e-road track on TORCS. \\ \end{tabular} & \
\begin{tabular}{c} Showed both lateral and \\ longitudinal motion control. \\ \end{tabular} &
\begin{tabular}{c} Lack of training data and \\ memory consuming. \\ \end{tabular} \\ \hline
[162] & RL with ACN & \begin{tabular}{c} Robust throttle and brake \\ value of the host vehicle. \\ \end{tabular} & \
\begin{tabular}{c} Learned controlling policy \\ while following lead vehicle. \\ \end{tabular} &
\begin{tabular}{c} Environmental surroundings \\ were not stated. \\ \end{tabular} \\ \hline
[163] & CIL & \begin{tabular}{c} 84\% success rate in \\ dynamic environment. \\ \end{tabular} & \
\begin{tabular}{c} Demonstrated successful \\ multimodal approach in \\ four cases. \\ \end{tabular} &
\begin{tabular}{c} Up to 18.37\% performance \\ drop in unknown map. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._obtained an RMSE value of 0.52, 0.07 and 0.23 for cases 1 to 3, respectively, and 21% safety enhancement adding hazard avoiding method. [PERSON] et al. proposed an efficient RRT algorithm that implemented a policy framework based on the traffic scenes and an intense search tree extension strategy to tackle traditional RRT problems where it faced a meandering route, an unreliable terminal state and sluggish exploration, and established more sustainable motion planning for AVS [169]. In addition, the integrated method of the proposed fast RRT algorithm and the configuration time space could be adopted in complex obstacle-laden environments to enhance the efficiency of the expected trajectory and re-planning. A significant set of experimental results showed that the system was much quicker and more successful in addressing on-road autonomous driving planning queries and demonstrating its better performance over previous approaches. In another work, an optimum route planner integrated with vehicle dynamics was designed by [PERSON] et al. implementing an artificial potential field to provide maximum workable movement that ensured the stability of the vehicle's path [170]. The obstacles and road edges were typically used with constraints and not with any arbitrary feature in this method in the optimal control problem. Therefore, when designing the optimum route using vehicle dynamics, the path-planning method was able to treat various obstacles and road structures sharply in a CarSim simulator. The analysis showed that the method reduced computational costs by estimating convex function while path planning. A similar method was proposed by [PERSON] et al., where they used an artificial potential field with adaptive multispeed scheduler for a collision-avoidance motion planning strategy [171]. [PERSON] et al. demonstrated a novel method combining CNN, LSTM and state model which was an uncertainty-aware vision-based trajectory generation network for AVS's path-planning approach in an urban traffic scene [172]. The work was divided into two major parts: the first one was a CNN bottleneck extractor, and the second component included a self-attention module for calculating recurrent history and an LSTM module for processing spatiotemporal characteristics. Finally, they designed the probable collision-free path planning with speeds and lateral or longitudinal locations for the next 3.0 s after taking image stream and state information in the past 1.5 s considering as input. The method obtained more centralized error distribution and lower error medium. For safe navigation for AVS in road scenarios with obstacles, a model prediction control-based advanced dynamic window (ADW) method was introduced by [PERSON] et al. [173]. The method demonstrated differential drive that reached the destination location ignoring the desired orientation and did not require any weighted objective function.