# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### End-to-End Controlling and Prediction  
End-to-end controlling is one of the major fields of study for AVS. Human mistakes were the main cause of road accidents, and fully autonomous vehicles can help reduce these accidents. To improve the control system of AVS analyzing driving scenarios for lane changing, [PERSON] et al. [144] proposed a system that tried to approximate driver's actions based on the data obtained from an uncertain environment that were used as parameters while transferring to parameterized stochastic bird statecharts (stohChart(p)) in order to describe the interactions of agents with multiple machine learning algorithms. Following that, a mapping approach was presented to convert stohChart(p) to networks of probabilistic timed automata (NPTA) and this statistical model was built to verify quantitative properties [145]. In the learning case, weighted KNN achieved highest accuracy combined with the proposed method considering training speed and accuracy, where it achieved 85.5% accuracy in 0.223 s and in the best case, time cost for probability distribution time for aggressive, conservative and moderate driving styles was 0.094, 0.793 and 0.113 s, respectively. The authors categorized \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[133] & GBDT & Calibration approach & Understand the key impacts of multiple variables on acceleration of fusion. & Implemented on old dataset and driver features were not analyzed. \\ \hline
[134] & DRL using Q-masking & 91\% success rate with 24\% lower collision rate. & Effective on high-level policies learning through conventional optimization. & Did not analyze real-time and complex road challenges. \\ \hline
[135] & Rule-based DQN policy & Safety rate 0.8 on average speed and lane-changing time. & Productive data alternative to end-to-end policy learning on challenge for high-level policy. & Required explicit driving path while training that caused low performance in complex scenes. \\ \hline
[136] & DQN & Collision rate decreased from 61.29\% to 0\%. & Efficient and quick emergency braking in complex environment. & Ambiguous training and environment setting. \\ \hline
[137] & HMGRM & Achieved specificity 97.41\% and 89.41\% accuracy. & Highly accurate braking action from driving situation. & Analysis of kinematic characteristics for both host and lead vehicle was missing. \\ \hline
[138] & DRQN & Obtained maximum 64.48\% more reward than human. & Did not require a labelled dataset and only took screen pixel as input. & Training time consuming. \\ \hline
[139] & AGC based DRL & 98.69\% higher average mean reward at intersection. & Efficient on intersection scenarios and reduced DRL training time. & Showed few collisions and unfinished cases during intersection traverse. \\ \hline
[140] & CNN + DDPG & Lowest 0.48 SD for left turn across path. & Overcame drawback of single scenario with discrete behavior in intersections. & Only considered lateral maneuvers. \\ \hline
[141] & DRQN & 70\% success rate for collision free episodes. & Tackled high-level behavioral decision in pedestrian-filled environment. & Low accuracy. \\ \hline
[142] & DDQL and FRC & Maximum 94\% accuracy with stable reward. & Applied for four driving decision for navigation avoiding obstacle. & Limited decision making for vehicles in parallel sides. \\ \hline
[143] & RL + MCTS & 100\% collision free episodes. & Outperformed DRQN and MOBIL method for safe lane shifting & Did not consider urban scenarios. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._their work into learning phase, modelling phase and quantitative analyzing phase in order to develop the driving decision-taking phase. A method was demonstrated by [PERSON] et al. to control independently at high speeds using human-like imitation learning, involving constant steering and acceleration motions [146].