Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

Investigation on friction-induced judder of vehicle driveline based on a nonlinear friction model

Due to friction characteristics of clutch, the driveline is prone to cause a judder during vehicle starting, and then to cause the vehicle body to vibrate, which affects driving quality. In order to analyze the judder phenomenon, a nonlinear numerical friction model based on the Gaussian friction model is established in this paper. For the driveline of a front-wheel-drive vehicle, a five-degree-of-freedom (5DOF) lumped parameter model including a nonlinear friction element is established. The complex mode of the driveline during the clutch in slip condition is calculated. The key parameters affecting the driveline stability are analyzed. The self-excited judder and pressure-induced judder of the driveline are numerically simulated, and the corresponding causes are analyzed. The nonlinear friction torque of the clutch is also calculated. Furthermore, the effects of the key parameters such as the torsional stiffness and damping of the clutch and drive shaft suppressing the self-excited judder and pressure-induced judder are numerically studied respectively. Compared with the widely used Karnopp friction model, the nonlinear numerical friction model established in this paper comprehensively includes the stribeck effect in slip and the friction torque characteristics in stick. The phenomena of the judder and stick-slip of the driveline during vehicle starting are more accurately simulated. The simulation results are in good agreement with the experimental results, which verify the accuracy and effectiveness of the dynamic model including the nonlinear friction element established in this paper.

Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

Phase Portrait Trajectory of a 3DOF Vehicular Dynamic Model

This article formulates a front-wheel-drive three-degree-of-freedom (3DOF) four-wheel planar vehicle model with the Magic Formula tire model. The state variables' evolutions of the model, i.e., trajectories of the model under acceleration and deacceleration conditions, are analyzed. The process of evolution is divided into desirable and undesirable phases based on the response characteristics of the vehicle to the driver input during the process. The trajectories are categorized as unsaturated trajectories and saturated trajectories by the existence of saturated tires during these phases. The response of state variables to driver input under acceleration conditions during undesirable phases are zero or even opposite, while the response of undesirable phases under the deacceleration condition is partially positive. Besides, the existing yaw rate safety envelope is recalibrated by using a longitudinal and lateral tire force coupling model. A more accurate yaw rate safety envelope is obtained from the given driver input. Furthermore, a longitudinal speed safety envelope is proposed according to the relationships among slip angle, yaw rate, and longitudinal speed. These safety envelopes are determined by driver input, tire properties, and grip condition. After overlaying yaw rate and longitudinal speed safety envelopes in the state space, the feasibility of using the safety envelope as trajectory classification criteria is discussed.

A Study on Highway Driving Assist Evaluation Method Using the Theoretical Formula and Dual Cameras

In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems using the theoretical formula and dual cameras, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required in such tests. A theoretical evaluation formula that can be calculated was proposed and used. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, HDA safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum errors were within 10%. It was determined that the representative cause of the maximum error occurred in the dual camera installed in the test vehicle. Problems such as road surface vibration, shaking due to air resistance, changes in ambient brightness, and the process of focusing the video occurred during driving. In the future, it is judged that it will be necessary to verify the complex transportation environment during morning and evening rush hour, and it is believed that tests will be needed in bad weather such as snow and rain.

Stability control of a vehicle with tire blowout based on active steering and differential braking

The vehicle with tire blowout will have dangerous working conditions such as yaw and tail flick, which will seriously endanger the safety of driving. A tire blowout model was established based on the UniTire model and the change of tire blowout mechanical characteristics. A Carsim/Simulink joint simulation platform was built to study the dynamic response of the vehicle after the front wheel tire blowout under curve driving. A combined control strategy of outer-loop trajectory control and inner-loop differential braking control based on sliding mode fuzzy control algorithms and fuzzy PID control algorithms was proposed to ensure that the vehicle can still follow the original trajectory stably after tire blowout. The results show that the tire blowout of the front wheel on the same side as the turning direction has a great influence on the instability and yaw of the vehicle, and the designed control strategy can effectively control the running track of the vehicle with tire blowout and the vehicle stability.

Design of vehicle using Ackermann steering with IoT concept

Electric vehicles are becoming more demanding these days. In this project the possibility of using Ackerman steering with electric drive servomotor is explained. Scalability is the advantage of using this mechanism which can be adopted for four-wheel vehicle system as well. The objective of this project is to do design a system using Ackerman steering which determines the maximum and minimum angle of the turning of the wheels. It also avoids the front tire slippage and activates pure rolling. Ackermann steering geometry is a geometric arrangement of linkages in the steering of a car or other vehicle designed to solve the problem of wheels on the inside and outside of a turn needing to trace out circles of different radii. The geometrical solution to this is for all wheels to have their axles arranged as radii of circles with a common centre point. As the rear wheels are fixed, this centre point must be on a line extended from the rear axle. Intersecting the axes of the front wheels on this line as well requires that the inside front wheel be turned, when steering, through a greater angle than the outside wheel. The microcontroller used in this project is ATMega16 andlmax232 is used for the serial data transmission.

Test Evaluation Method for Lane Keeping Assistance System Using Dual Cameras

Recently, the number of vehicles equipped with the Lane Keeping Assistance System (LKAS) is increasing. Therefore, safety evaluation to validate the LKAS has become more important. However, the actual vehicle test for safety evaluation has disadvantages such as the need for professional manpower, the use of expensive equipment, and environmental constraints. Therefore, we attempted to solve this problem using the dual cameras system with only inexpensive and accessible cameras. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, LKAS safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum error was 0.17 m, which indicated the reliability of the method because all errors in the tested scenarios exhibited similar trends and values. Therefore, through the use of the proposed theoretical equations in conjunction with inexpensive cameras, it is possible to reduce time, cost, and environmental problems in the development, vehicle application, and safety evaluation of LKAS components.