# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Decision Making
The partial dependency plots demonstrated that the effect on the fusion of acceleration or deceleration in independent variables by understanding the key impacts of multiple variables, was non-linear and thus distinct from the car tracking behavior with 0.3517 MAD (Mean Absolute Deviation) value, which suggested that the adoption of typical vehicle models in combination results cannot reflect characteristic behavior. Further, DRL with Q-masking was applied by [PERSON] et al. to make tactical decisions for shifting lanes [134]. They introduced a system which provided a more organized and data-efficient alternative to a comprehensive policy learning on issues where high-level policies are difficult to formulate through conventional optimization or methods based on laws. The success rate of 91% was 21% higher than human perception and the 0% collision was 24% lower than human perception. This method of DRL with Q-masking worked best in the case of avoiding collision while lane shifting. Similarly, [PERSON] et al. adopted DRL but combined with rule-based constraints to take lane-changing decisions for AVS in a simulated environment and MDP, which was challenging for high-level policy to develop through conventional methods of optimization or regulation [135]. The training agent could take the required action in multiple situations due to the environment of state representation, the award feature and the fusion of a high level of lateral decision making and a rule-based longitudinal regulation and trajectory adjustment. The method was able to obtain a 0.8 safety rate with superior average speed and lane-changing time. [PERSON] et al. demonstrated an emergency braking system applying DQN [136]. The problem of brake control model was conceived in [PERSON]'s decision-making process (MDP), where the status was provided by the relative location of the hazard and the speed of the vehicle and the operating space specified as the collection of brake actions including no braking, weak, medium and heavy braking operation, combining vehicle, pedestrian and multiple road conditions scenarios, and the obtained collision rate decreased from 61.29% to 0% for a TTC value from 0.9 s to 1.5 s. As a result, this DQN-based approach was selected as one of the most practical systems for SVM in terms of autonomous braking. Furthermore, to analyze high-accuracy braking action from a driving situation declaring four variables, that is, speed of host vehicle, time to collision, relative speed and distance between host and lead vehicle, [PERSON] et al. used hidden Markov and Gaussian mixture-based (HMGM) approach [137]. The efficient technique was able to obtain high specificity and 89.41% accuracy despite not considering kinematic characteristics of lead or host vehicle for braking. However, the analysis of four variants while braking could be a pathway to develop an improved version of braking decision making for AVS. When most of the approaches had dependency on datasets, methods such as DRL that combined DL and RL were extremely efficient for driving decision making in an unknown environment. For example, [PERSON] et al. developed a brain-inspired simulation based on deep recurrent reinforcement Q-learning (DRQL) for self-driving agents with better action and state space inputting only screen pixels [138]. Although the training process was long, it resulted in better-than-human driving ability and Stanford driving agent in terms of reward gain, which indicates that this approach was one of the most suitable for applying in AVS. Another DRL-based approach combined with automatically generated curriculum (AGC), was extremely efficient for intersection scenarios with less training cost [139]. The method obtained 98.69% and 82.1% mean average reward while intersection approaching and traverse. However, the approach might lack proper finishing or goal researching in some cases of intersection traverse, but it is still very efficient for not depending on pre-trained datasets.