# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Longitudinal Control of Autonomous Vehicles
#### 2.2.2 Longitudinal Model-Free Control Techniques  
The function of longitudinal control is the automated guidance. Therefore, the longitudinal control regulates the autonomous vehicle (AV) speed to ensure comfort and safety. The labeled training data are collected in simulation environments or the real world to estimate the right speed using DL as a longitudinal control technique. In [66], a neural network-based control was developed for intersections in autonomous vehicles, aiming to reduce computational complexity in motion optimization. The neural network was trained using an offline solution, and a robustness analysis examined the impact of speed and position estimation errors. CarSim simulations demonstrated the design and analysis, highlighting the trade-off between robust scenarios, position estimation error, and the need to balance energy loss and sensor network costs. In [67], a sensorless state estimation approach was presented for brake pressure in vehicles using deep learning, with the goal of achieving high autonomy and safe driving. The structured deep neural network (DNN) was trained using techniques such as rectified and dropout units, yielding a highly accurate model for brake-pressure state estimation. Training and validation were performed using experimental data collected from a real test vehicle connected to a chassis dynamometer. The effectiveness and applicability of the proposed technique were demonstrated, with a root mean square error (RMSE) of 0.048 MPa achieved for brake-pressure state estimation. In [68], an RL model was introduced for optimizing car-following performance by integrating comfort, efficiency, and safety factors. The RL agent learned vehicle speed control through simulations using a reward function derived from human driving data. Collision avoidance was incorporated for safety. Comparative evaluations with MPC-based ACC and empirical data showed that the proposed RL model outperforms in terms of time-to-collision values, headways, and comfortable following with smooth acceleration. It demonstrates potential for advanced autonomous driving systems. In [69], DRL and MPC were compared for ACC in car-following scenarios. A COM approximates vehicle dynamics. DRL (trained with DDPG) equals MPC with a long prediction horizon within the training data range and no modeling errors. DRL's episode cost is 5.8% higher than IPO-optimized benchmark. DRL's performance degrades outside training data range, indicating poor generalization. DRL matches MPC with small modeling errors and outperforms with large errors, disturbances, control delays, and high-fidelity models. Integration of DRL and MPC can leverage strengths and mitigate drawbacks. In [70], a multi-agent reinforcement learning approach for cooperative adaptive cruise control (CACC) in platooning vehicles was presented. The LSTM was trained with policy gradient for ACC implementation, allowing the LSTM to be adapted for information exchange and driving coordination. Simulations were conducted with two platoons of three and five vehicles, evaluating the CACC with the learned communication protocol against various communication baselines and a jamming attack. The approach incorporates local and global reward systems, demonstrating faster convergence and higher performance when utilizing the learned communication protocol and individual rewards. In [71], a method for predicting nearby vehicles' paths and controlling autonomous vehicles (AVs) in urban road conditions was introduced. The trajectory prediction was performed using a deep learning model based on long short-term memory (LSTM), incorporating the historical relationship between a target vehicle and lanes. The interaction among adjacent vehicles was captured through a graph convolutional network (GCN) with self-attention. The prediction model utilizes sensor data locally acquired from AVs. The determination of acceleration inputs is done by model predictive control (MPC), with a focus on prioritizing safety and ride quality.