Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This article introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this article provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

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An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Autonomous vehicles have to share the road with human-driven ones for a prolonged period in the near future. Thus, they should have the ability to understand the lane changing intentions of surrounding human-driven vehicles. Unlike human drivers, the state-of-the-art models used in autonomous vehicles recognize a driver's lane changing intention based on the target vehicle's lateral movement, which leaves little to no time for the autonomous vehicle to react. In this paper, a Human-like Lane Changing Intention Understanding Model (HLCIUM) for autonomous driving is proposed to understand the lane changing intentions of surrounding vehicles. By imitating the selective attention mechanism of human vision systems, the proposed model emulates the way human drivers concentrate on the surrounding vehicles and recognizes their lane changing intentions accordingly. The velocity changes of the surrounding vehicles are treated as the lane changing hints, and the attention is drawn to the corresponding vehicle following a saliency-based scheme. Then, the lane changing intention is identified by a Hidden Markov Model (HMM) based intention recognizer. The proposed model is tested with Next Generation Simulation (NGSIM) vehicle trajectory dataset, and the proposed method has reached 90.89% in detecting lane changing intention and 88.58% in lane keeping in urban road scenarios, and reached 87.73% and 87.48% for the lane changing and lane keeping intentions in highway scenarios, respectively. Importantly, the average recognition time before the lane changing maneuver of the proposed model is 6.67 seconds for the urban road datasets and 7.08 seconds for the highway datasets, which is far earlier than state-of-the-art models. Furthermore, the proposed method shows efficiency and robustness in complex real urban traffic datasets, which is ideal to use in human-like autonomous driving systems.

A Human-Like Model to Understand Surrounding Vehicles’ Lane Changing Intentions for Autonomous Driving

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

https://dspace.lib.cranfield.ac.uk/bitstream/1826/18424/1/Driver_digital_twin-2022.pdf

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Recently, inferring lane change intention has received considerable attention. Due to the high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the requirements of long-term prediction tasks and lack the ability of capturing nonlinear temporal dependencies. This paper proposes an intention inference model based on Recurrent Neural Networks (RNN), to tackle time series prediction problems. Considering dynamic interaction among surrounding vehicles, our model takes the sequence motion information of surrounding vehicles as inputs and calculates the congestion of different lanes, integrated with vehicle states of the object vehicle. To illustrate the availability of the proposed RNN intention inference model, a motion planning controller considering intention was developed. A Nonlinear Model Predictive Control (NMPC) was established to planning a safe, sub-optimal path for autonomous driving vehicle under collision avoidance constraints. The experiments on the proposed model were conducted, based on two RNN structure Long-Short Term Memory (LSTM) and Generalized Recurrent Unit (GRU), by Tensorflow with NGSIM data. The motion planning controller is modeled and simulated by Carsim with Simulink for some typical scenarios. Subsequently, experimental results demonstrate that RNN achieves best performance, inferring intention with 96% accuracy, compared with other approaches.

Lane-Change Intention Inference Based on RNN for Autonomous Driving on Highways

In this paper, an improved Model Predictive Control (MPC) controller based on fuzzy adaptive weight control is proposed to solve the problem of autonomous vehicle in the process of path tracking. The controller not only ensures the tracking accuracy, but also considers the vehicle dynamic stability in the process of tracking, i.e., the vehicle dynamics model is used as the controller model. Moreover, the problem of driving comfort caused by the application of classical MPC controller when the vehicle is deviated from the target path is solved. This controller is mainly realized by adaptively improving the weight of the cost function in the classical MPC through the fuzzy adaptive control algorithm. A comparative study which compares the proposed controller with the pure-pursuit controller and the classical MPC controller is made: through the CarSim-Matlab/Simulink co-simulations, the results show that this controller presents better tracking performance than the latter ones considering both tracking accuracy and steering smoothness.

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Path Tracking Control for Autonomous Vehicles Based on an Improved MPC

Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).

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Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

In the trend of urgent demand of energy saving for public transportation, the series-parallel plug-in hybrid electric bus (SPPHEB) with energy saving potential is proposed. The fuel economy of hybrid power train depends to a significant degree on its control strategy, and then an energy management strategy based on nonlinear model predictive control (NMPC) is obtained for better fuel economy performance. Firstly, the quasi-static model of the plant is described and the reference curve of the state of charge (SOC) and the predictive torque are formulated and illustrated. Then the NMPC framework for SPPHEB, which explored the torque prediction method in the prediction domain and the prediction method of the reference SOC trajectory on the whole working condition, is introduced and completed by adopting dynamic programming (DP) algorithm to solve the nonlinear optimization problem. Finally, the NMPC strategy is simulated in Simulink, and its optimization performance is compared with other strategies such as DP, equivalent consumption minimization strategy (ECMS) and charge-depleting and charge-sustaining (CDCS). The simulation result is that compared with the CDCS strategy, NMPC strategy shows an economic improvement by 18.86%, and 10.36% improvement compared with the ECMS strategy. The good performance of the NMPC strategy is due in part to the consideration of the reference SOC trajectory mechanism and the prediction of the expected torque. The NMPC-based EMS considered both optimization performance and computation burden, which may provide a prospect for further practical application of real vehicles.

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Nonlinear Model Predictive Control for Series-Parallel Hybrid Electric Buses

An advanced LKS (lane keeping system) for use on curving roads is presented to maintain autonomous vehicle driving within the target lane, without unintentional lane departure. There are the following two main objectives in designing this system: one is performing perfect lane keeping and the other is ensuring the dynamic stability of the vehicle, especially when driving on a curving and low-friction road with time-varying high speed. In this paper, a combined vehicle model, consisting of a lane keeping model and a vehicle lateral dynamic model, is firstly introduced. Then, a novel adaptive-weight predictive controller is used to calculate the desired steering angle and the additional yaw moment which provide coordinated control forlane keeping and dynamic stability control. Meanwhile, a square-root cubature Kalman filter-based vehicle sideslip angle observer, with a strong tracking theory modification (ST-SRCKF), is established to estimate the sideslip angle during the driving process. Finally, HIL (hardware-in-the-loop) tests and field tests are constructed, and the results show the effectiveness of our proposed LKS controller and ST-SRCKF sideslip angle estimation.

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Hengyang Wang

Papers

Path Tracking Control for Autonomous Vehicles Based on an Improved MPC

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Nonlinear Model Predictive Control for Series-Parallel Hybrid Electric Buses

Advanced High-Speed Lane Keeping System of Autonomous Vehicle with Sideslip Angle Estimation

Real-Time Energy Economy Optimization Based on Nonlinear MPC for Hybrid Electrical Buses