# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Path and Motion Planning  
Precipitation-based autonomous navigation including path and motion planning in an unknown or complex environment is one of the critical concerns for developing AVS. To tackle the current problem and analyze the contribution, multiple deep learning and deep reinforcement learning (DRL) combined methods for path and motion planning are reviewed in this section. Initially, [PERSON] et al. focused on the issue of path planning of autonomous vehicles in traffic in order to repeat decision making by replicating the optimum driving technique of expert drivers' actions for lane changing, lane and speed maintenance, acceleration and braking in MDPs on highways [164]. The optimal control policy for the proposed MDP was resolved using deep inverse reinforcement learning (DIRL) and three MaxEnt IRL algorithms by utilizing a reward function in terms of a linear combination of parameterized function to solve model-free MDP. The trajectory proposals were executed at the time of overtaking and the policy recovery was reduced to 99%, even though there was insufficient evidence for the reflection of stochastic behavior. To solve limitations of rule-based methods for safe navigation and better intersection problems for AVS, a vision-based path and motion planning formula was used by [PERSON] et al., adopting DRL [165]. Each wait action was proceeded by another wait or go action, meaning that each pathway was a series of waiting decisions that concluded in a go decision as well as the agent not being permitted to wait after the go action had been chosen. The method secured a success rate for forward, right, left and turn and challenge of 99.96%, 99.99%, 99.78% and 98.46%, respectively, which was 28% faster than the TTC (time-to-collision) method, although performance decreased three times and average time doubled during this challenging situation. [PERSON] et al. proposed a risk analysis and motion planning system for autonomously operated vehicles focused on highway scenario motion prediction of surrounding vehicles [166]. An interactive multiple model (IMM) and constant turn rate and acceleration (CTRA) model were used for surrounding vehicle motion prediction, and model predictive control (MPC) was used for trajectory planning that scored 3.128 RMSE after 5 s during motion prediction. Although it was designed for connected AVS, it is efficient for vision-based approaches. Another approach, local and global path planning methodology, was presented in an RoS-based environment for AVS by [PERSON] et al., where they used the Dijkstra and time elastic bands (TEB) method [167]. The path planning model was able to reach the goal with modest error by calculating Euclidean distance for comparing local and global pan waypoints, where it scored 1.41 m, which is very efficient. However, it was applicable only if the model was not specifically calibrated for the vehicle's kinematics or if the vehicle was out of track, and did not consider complex scenarios. In another work, [PERSON] et al. established a vision-based autonomous driving system that relied on DNN, which handled a region with unforeseen roadway hazards and could safely maneuver the AVS in this environment [168]. In order to overcome an unsafe navigational problem, they presented object detection and structural segmentation-based deep learning architecture, where it