Objective

2

Path tracking is the key part of an automatic driving vehicle running along a road according to the perception system and decision system results. The control module involved in path tracking is the lowest-level software algorithm module of autopilot

which includes two parts: lateral control and longitudinal control. Steering control is mainly responsible for vehicle steering output control

and longitudinal control is mainly responsible for throttle and brake control. The steering control algorithm tracks and controls the path of the two upper frameworks on the basis of perception and decision

and it optimizes the tracking error to ensure the stability and comfort of the self-driving vehicle. Current tracking algorithms mainly include model-free lateral control algorithm and model-based lateral control algorithm. The representative of model free lateral control algorithm is proportion integration differentiation (PID) control. The PID algorithm is difficult to use in controlling automatic driving vehicles without considering the physical characteristics of the vehicle and in high-speed and complex environments. The model-based lateral control algorithm includes the lateral control algorithm based on vehicle kinematics model and the lateral dynamics algorithm based on vehicle dynamic model. The former is represented by the Stanley method based on front-wheel feedback and rear-wheel control based on rear-wheel feedback. The latter is represented by the lateral control algorithm of linear quadratic regulator based on the dynamic model. Algorithms based on the vehicle model need to accurately model the kinematic or dynamic characteristics of the vehicle and usually need to simplify the model to predict the state of vehicle tracking deviation by simplifying the modeling of the model. This approach thus achieves accurate control of the vehicle. In addition

these two lateral control algorithms do not consider the coupling characteristics with longitudinal control

which limits the tracking performance of the control algorithm. To address the problems of high-complexity vehicle model and current path tracking algorithm

this paper proposes a model free-steering control algorithm based on speed-adaptive preview.

Method

2

In the face of highly complex or unknown dynamic performance of the vehicle dynamics model

accurately calculating the state equation of vehicle path tracking deviation through dynamic characteristics is impossible. However

due to the complex dynamic characteristics

the control quantity obtained through kinematic characteristics will cause many errors

especially in the case of high-speed driving

large curvature

and nondifferentiable path. The cumulative error of these two methods may lead to a self-driving car going out of control. Therefore

the model-free control method can achieve stable and accurate path tracking performance in the full speed range under complex dynamic conditions. Considering the stable tracking in scenes with non-conductance and large curvature

this paper uses the speed-adaptive preview method to enhance the stability of autopilot under complex road conditions. According to the intelligent driving vehicle model studied in this paper

the control input includes the lateral distance difference between the vehicle and the tracking path

the angle between the vehicle and the tracking path

and the coupling parameters of the longitudinal speed of the vehicle. The output of the control algorithm includes the steering wheel angle

the throttle opening

and the brake master cylinder pressure. The former mainly controls the direction of the vehicle

while the latter controls the forward speed. In this paper

the output control equation of vehicle steering control is established first according to the deviation distance and angle between the vehicle and the tracking path. This method realizes stable tracking at low speed under the condition of highly complex dynamics and differentiable tracking path. At the same time

the tracking preview distance is set adaptively according to the vehicle longitudinal speed

and the speed coupling parameters are added to the equation to realize the stable tracking of the vehicle in the full speed range and all types of paths.

Result

2

To verify the proposed path tracking algorithm based on speed-adaptive preview

we participated in the 2020 China Intelligent Vehicle Championship and World Intelligent Driving Challenge online simulation competitions. In this experiment and competition

PanoSim automatic driving simulation system and Simulink simulation software

are used for the simulation experiment. The test road is a 10 km test freeway provided by Panosim

and it includes five sections with large curvature and five sections with small curvature. In this paper

we select a typical section of the small curvature section and large curvature section to test the algorithm. Under the dynamic model with a high-degree-of-freedom dynamic model

the proposed algorithm can achieve the performance of lateral deviation |Δ

d

|

<

0.1 m on the ultra-high speed (

>

220 km/h) straight-line

and small-curvature tracking path

and |Δ

d

|

<

0.3 m on the high-speed (

>

150 km/h) high-curvature curve tracking path.

Conclusion

2

In this paper

the path tracking algorithm based on speed preview is proposed

and the model free lateral control algorithm is studied to realize the control coverage from a simple to a complex vehicle model; the optimization of vehicle lateral control by a speed coupler is studied to realize the full speed range control coverage of the vehicle; and the controller based on speed-adaptive preview is studied to realize the transition from the differentiable path to the nondifferentiable path. To some extent

the problem of control hysteresis and overshoot is solved.