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.

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).

How to plan the collision-free path effectively is crucial for autonomous ground vehicles to avoid collisions. The vehicle may lose its lateral dynamic stability due to high speed, time-varying speed and multifriction road during tracking the collision-free path. In this article, an advanced collision avoidance framework is proposed to avoid collision efficiently with road friction estimation and dynamic stability control. First, an improved A* algorithm is constructed to generate a desired trajectory for collision avoidance, which is capable of observing road regulations and overcoming the vehicle mechanical drawbacks. Next, a model predictive control-based path-tracking controller is established to solve the tracking task as a multiconstraint and multitarget optimization problem, where optimized steering angle and additional yaw moment can be calculated. Meanwhile, a novel long short-term memory based road friction coefficient estimator is built to observe road friction. Moreover, electric power steering system controls the steering motor to realize the desired steering angle. The additional yaw moment can be obtained by differential braking. Finally, hardware-in-the-loop platform tests are conducted to validate that the proposed controller can not only avoid collisions effectively, but also have a good performance on keeping the vehicle dynamic stability with accurate road friction estimation.

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.

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.

Lane detection is an essential task in autonomous driving. A good lane detection model should achieve many objectives, such as high accuracy, rapid detection, and low memory. In this article, a grid-based network (G-NET) is designed to realize the aforementioned goals. In G-NET, the traditional pixel-level semantic segmentation is replaced with the area-level grid segmentation to release the detection burden. Then, a position vector is introduced to indicate where lane key point is in the grid. Meanwhile, the novel rolling convolution layer following with the down-sampling and up-sampling convolution layer has been designed for good feature extraction, ensuring each feature grid perceives all other grid features in the feature map. Then, an adaptive hyperparameter branch is introduced to calculate the binary threshold effectively. Finally, the detected lane points are classified into different lanes by introducing distance-based quaternion. G-NET is extensively evaluated on three most widely datasets: TuSimple, CULane, and CurveLanes. The results show that G-NET has a state-of-the-art performance. Meanwhile, field tests are conducted.