# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### End-to-End Controlling and Prediction
The dataset's reference policy was derived from a costly high-resolution model predictive controller, which the CNN subsequently trained to emulate using just low-cost camera sensors for observations. The approach was initially validated in ROS Gazebo simulations before being applied to a real-world 30 m-long dirt track using a one-fifth-scale car. The sub-scale vehicle successfully learnt to navigate the track at speeds of up to 7.5 m/s. [PERSON] et al. focused on a lane-keeping end-to-end learning model predicting steering angle [147]. The authors employed CNN to the current NVIDIA Autonomous Driving Architecture, where both incorporated driving image extraction and asserting steering angle values. To test the steering angle prediction while driving, they considered the difference among ground truth angle which was generated by human drivers vs. predicted angle where they acquired higher steering prediction accuracy with 2.42 mean absolute error and suggested for data augmentation for training to achieve a better performance. In another work, a technically applied system of multitask learning in order to estimate end-to-end steering angle and speed control, was proposed in [148]. It was counted as one of the major challenging issues for measuring and estimating speed only based on visual perceptions. Throughout their research, the authors projected separation of speed control functions to accelerate or decelerate, using the front-view camera, when the front view was impeded or clear. Nevertheless, it also showed some shortcomings in precision and pre-fixed speed controls. By combining previous feedback speed data as a complement for better and more stable control, they improved the speed control system. This method could be stated to solve error accumulation in fail-case scenarios of driving data. They scored 1.26\({}^{\circ}\) Mean Absolute Error (MAE) in estimating real-time angles along with 0.19 m/s and 0.45 MAE on both datasets for velocity prediction. Thus, the improved result made the method one of the most applicable versions of CNN and data-driven AV controlling. While driving, people identify the structures and positions of different objects including pedestrians, cars, signs and lanes with human vision. Upon recognizing several objects, people realize the relation between objects and grasp the driving role. In the spatial processing of single images by the application of three-dimensional vectors, CNN has certain shortcoming in the study of time series. However, this issue cannot be overcome using CNN alone. To solve this limitation [PERSON] et al. demonstrated an end-to-end self-driving control framework combining a CNN and LSTM-based time-series image dataset applied in a Euro Truck simulator [149]. The system created a driving plan which takes the changes into account over time by using the feature map to formulate the next driving plan for the sequence. Moreover, NVIDIA currently has succeeded in training a ConvNet for converting raw camera images into control steering angles [150]. It resolved end-to-end control by predicting steering angle without explicating labels with approximately 90% autonomy value and 98% autonomous of the testing period. This approach was one of the most demonstrated approaches that boosted research of AVS applying deep learning methods. A similar method, deep ConvNet, was used by [PERSON] et al. to train for directly extracting the identified accessories from the front camera [151]. A basic control system, based on affordance principles, provided steering directions and the decision to overtake proceeding vehicles. Rather than using lane-marking detection methods as well as other objects to assess indirect activity specifications of the car, a variety of driving measures allowances were specified. This method included the vehicle location, the gap to the surrounding lane markers and records of previous car driving.