# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### End-to-End Controlling and Prediction
They therefore sampled the estimated distribution from this space in a way which helped the method to monitor the representation of semantically different scenarios. A CNN and state-transitive LSTM-based approach was demonstrated with multi-auxiliary tasks for retrieving dynamic temporal information from different driving scenarios to estimated steering angles and velocity simultaneously [158]. The method applied the vehicle's current location to determine the end-to-end driving model sub-goal angle to boost the steering angle estimation accuracy, which forecasted that the efficiency of the driving model would improve significantly. The combined method obtained 2.58\({}^{\circ}\) and 3.16\({}^{\circ}\) MAE for steering angle prediction and 0.66 m/s and 0.93 m/s speed MAE in GTAV and Guangzhou Automotive Cooperate datasets, respectively. Nevertheless, it showed a slow response in unknown environment, so this method might not be applicable in practical implementation. In a similar manner, [PERSON] et al. presented a CNN-based model for lateral control of AVS using a fisheye camera with label augmentation technique for accurate corrections labelling under lateral control rule to tackle ceases of lateral control error in wide FoV [159]. This method compares with pure offline methods where feedback was not implemented from a prediction which resulted in 99.5% and 98.7% autonomy in urban areas and highways after training with 10,000 km and 200 h driving video. On the other hand, [PERSON] et al. reduced a huge number of parameters of CNN to avoid overfitting along with helping to find dependency on data sequence and implement in a CarND Udacity Simulator for predicting steering angles. However, the obtained unsatisfactory result was comparable to other reviewed results, where the accuracy was 78.5% [160]. Similarly, a CNN-based approach was applied for both lateral and longitudinal motion controlling of AVS obtaining 100% autonomy on e-road track on TORCS simulator. Although it had performed very well, contributing to both kinds of motion controlling, it lacked training data for practical implementation and memory consumption for training two different neural networks for speed and steering angle prediction. This method could be better approached by implementing in real scenarios with a good amount of training data [161]. In another proposal, a reinforcement learning-enabled throttle and brake control system was proposed by [PERSON] et al. [162], focusing on a one leader and one follower formation. A neural dynamic programming algorithm evaluating with trial-and-error method was directly applied for adopting near-optimal control law. The control policy included the necessary throttle and brake control commands for the follower according to the timely modified corresponding condition. Simulation experiments were carried out using the well-known CarSim vehicle dynamic simulator to show the reliability of the approach provided. To overcome traditional sensor-based pipeline for controlling AVS where there is a tendency to learn from direct mapping, [PERSON] et al. demonstrated multimodal end-to-end AVS applying conditional imitation learning (CIL), taking an RGBD image as raw data in a Carla simulator environment [163]. The CNN-based CIL algorithm was evaluated in different weather modes to identify the performance for end-to-end control. The success rate of controlling in one turn and dynamic environment were 95% and 84%, respectively, which could be boosted through early fusion by changing the number of color channels from three (RGB) to four (RGBD). However, performance dropped almost 18.37% and 13.37% during controlling AVS with RGB image input for one turn and dynamic environment, respectively, in a new map of Carla simulators which could be considered as uncertain area  
In brief, most of the deep learning approaches for end-to-end controlling and motion predications were based on CNN, showing efficient outcomes suitable for practical level 4 or 5 AVS.