# Adaptive Pre-Aim Control of Driverless Vehicle Path Tracking Based on a SSA-BP Neural Network

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## Abstract

**:**

## 1. Introduction

## 2. Vehicle Model

## 3. SSA-BP-Neural-Network-Based Adaptive Pre-Aim Control for Path Tracking

#### 3.1. Overall Control Scheme

#### 3.2. Directional Control Driver Model

#### 3.2.1. Pre-Aim Control Model

#### 3.2.2. Steering Wheel Cornering Decision Mechanism

#### 3.3. SSA-BP Neural Network Pre-Aim Time Adjuster

#### 3.3.1. BP Neural Network

#### 3.3.2. Sparrow Search Algorithm (SSA)

#### 3.3.3. Pre-Aim Time Adjuster

- Firstly, the topology of the BP neural network and the input and output variables are determined, and the algorithm stopping condition is set as the maximum error value ${e}_{train}\le {e}^{-4}$;
- The weights and thresholds of the BP neural network are initialized, and the parameters related to the SSA algorithm, such as ($ite{r}_{max}$, $\tau $, $ST$, etc.), are defined;
- The fitness values of the sparrow population are calculated and ranked from smallest to largest to find the current optimal and worst values, where the initial fitness value is the training error of the random initial value of the BP neural network;
- Equations (20)–(22) are used to update the location of finders, joiners, and vigilantes, respectively;
- The optimal value of the sparrow position for this iteration is obtained, and the position is updated if the new position is better than the optimal value of the previous iteration; otherwise, no position update is performed;
- If the maximum number of iterations $ite{r}_{max}=100$ is reached, the search is stopped, and the global optimum and the best fitness value are output; otherwise, steps 3–5 are repeated;
- The parameters corresponding to the global optimum of the SSA algorithm are used as the initial weights and thresholds of the BP neural network, and the network is trained by inputting training and testing sample sets.

## 4. Simulation Experiments and Analysis

#### 4.1. Variable-Speed Continuous Three-Curve Roads Tracking Experiment

#### 4.2. Alt 3 Road Tracking Experiment

## 5. Conclusions

- Based on the vehicle model and the pre-aim control model, we built a “human–vehicle–road” closed-loop control system and proposed a new directional control driver model;
- The offline optimization of BP neural network primaries by the sparrow search algorithm (SSA) improves the prediction accuracy of the pre-aim time. Considering the law of pre-aim time selection under different driving conditions, the controller established by using the directional control driver model was used to conduct simulation experiments on the reference path (U-turn path). The SSA-BP neural network was trained with the obtained parameter samples to achieve the dynamic selection of pre-aim time;
- The longitudinal variable-speed controller was designed to reduce the coupling effect of longitudinal speed on path tracking, making the method in this paper more adaptable to a wide range of speed variation and able to achieve accurate path tracking under a variety of variable-speed working conditions;
- Through the tracking simulation experiments on two different roads, the results show that the maximum error value of the method in this paper is only 0.2673 m under the fixed-speed and variable-speed conditions, providing a new scheme for the research of path tracking pre-scanning control.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Block diagram of adaptive pre-aim control of path tracking based on an SSA-BP neural network.

**Figure 6.**Test sample verification results: (

**a**) Comparison of predicted values and true values. (

**b**) Predicted error values.

**Figure 7.**Variable-speed continuous-curve simulation results: (

**a**) Longitudinal speed change curve of the vehicle. (

**b**) Path tracking with different methods. (

**c**) Lateral displacement tracking deviation. (

**d**) Adaptive change in pre-aim time.

**Figure 8.**Alt 3 simulation results at different speeds: (

**a**) 36 km/h path-tracking effect. (

**b**) 36 km/h lateral tracking error. (

**c**) 72 km/h path-tracking effect. (

**d**) 72 km/h lateral tracking error. (

**e**) 108 km/h path-tracking effect. (

**f**) 108 km/h lateral tracking error.

**Figure 9.**Simulation results of variable speed on the Alt 3 road: (

**a**) Variable-speed path-tracking effect. (

**b**) Variable-speed lateral tracking error.

Algorithm | Maximum Error | MAE | RMSE |
---|---|---|---|

BPNN | 0.1362 | 0.01583 | 0.047 |

GWO-BPNN | 0.07554 | 0.004989 | 0.02331 |

SSA-BPNN | 0.06156 | 0.002059 | 0.0229 |

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## Share and Cite

**MDPI and ACS Style**

Huang, Y.; Luo, W.; Lan, H.
Adaptive Pre-Aim Control of Driverless Vehicle Path Tracking Based on a SSA-BP Neural Network. *World Electr. Veh. J.* **2022**, *13*, 55.
https://doi.org/10.3390/wevj13040055

**AMA Style**

Huang Y, Luo W, Lan H.
Adaptive Pre-Aim Control of Driverless Vehicle Path Tracking Based on a SSA-BP Neural Network. *World Electric Vehicle Journal*. 2022; 13(4):55.
https://doi.org/10.3390/wevj13040055

**Chicago/Turabian Style**

Huang, Yinggang, Wenguang Luo, and Hongli Lan.
2022. "Adaptive Pre-Aim Control of Driverless Vehicle Path Tracking Based on a SSA-BP Neural Network" *World Electric Vehicle Journal* 13, no. 4: 55.
https://doi.org/10.3390/wevj13040055