In the most of previous studies, social interactions between vehicles are not considered explicitly when designing decision making and motion planning module. Nevertheless, the strong interaction exists in the most of driving scenarios, especially in highway junction. This paper presents a game theoretic merging behavior control system for autonomous vehicle at on-ramp junction considering the interaction between the merging vehicle and following vehicle in the main lane. In this work, a driving style estimation is proposed to deal with the heterogeneity of driving style. Then we adopt a model predictive control (MPC) method to plan the optimal merging trajectory based on the game theoretic decision making result. Finally, simulation and Human-in-the-loop (HIL) experiment result shows the effectiveness of our approach in on-ramp merging scenario.

In this article, we propose a novel personalized planning and control approach for lane change assistance system which can efficiently learn a model prediction control (MPC)-based driver-specific lane-changing policy via end-to-end imitation learning from a few driver demonstrations. Specifically, we build a novel learnable predictive model of the vehicle-driver system and design an adaptable cost function for the MPC-based lane change controller. We then calculate the gradient of the imitation loss with respect to the personalization parameters of the model and cost function via differentiating the optimality conditions, and update those parameters to minimize the imitation loss in an end-to-end fashion. A semi-physical simulation on a driving simulator and a closed-loop test on a real vehicle are conducted to validate the learning ability and personalized control performance. The results show that 1) the proposed method can automatically implement both the generalized and the personalized lane change planning and control by learning from demonstration data; 2) the proposed controller can adapt to different driver-specific behaviors; and 3) the proposed approach outperforms the model-free learning approach in terms of imitation accuracy, interpretability, data efficiency, and generalized performance.

It is a great challenge to guarantee both path-tracking performance and vehicle stability when suffering from aggressive uncertainties and severe disturbances. We design a novel learning-based robust model predictive path-tracking controller to alleviate the influence of disturbances, avoid over-conservative steering actions, and mediate the conflict between path-tracking and vehicle stability. Specifically, we firstly utilize the friction limits of tires and define an enveloped stable zone in the phase portrait which is used as safety constraints. Secondly, two approaches under the model predictive control (MPC) framework are employed to tackle the severe uncertainties and disturbances: 1) a deep neural network (DNN) dynamics model is employed to estimate the predictive error and attenuate the mismatch between the nominal model and actual plant; and 2) a local feedback linear quadratic regulator (LQR) is used to stabilize the system matrix, calculate invariant tube, and thus guarantee all state and control constraints are satisfied. Finally, real vehicle experiments indicate that the proposed controller can achieve an over 18% improvement in path-tracking performance and guarantee vehicle stability, even for cases with severe uncertainties and disturbances.

In the process of autonomous vehicle lane changing, a reliable decision-making system is crucial for driving safety and comfort. However, traditional decision-making systems have short-term characteristics, which makes them susceptible to real-time inference from surrounding vehicles. Usually, system sacrifices driving comfort to ensure the safety of the lane change. Balancing driving safety and comfort has always been a research challenge. Long-term trajectory prediction can provide accurate future trajectories of target vehicles, providing reliable long-term information to compensate for the short-term variability of decision systems. This paper proposes a novel decision-making model with long-term trajectory prediction for lane-changing. First, we constructed a long-term trajectory prediction model to predict the trajectories of surrounding vehicles. Besides, we built a lane change decision-making model based on fuzzy inferencing, considering the predicted trajectories to infer the relative relationship between other vehicles and the self-driving car. The establishment of the fuzzy rule library considered the vehicle speed, acceleration, system delay time, driver delay time and the distance between vehicles. Finally, we created a dataset for training and testing the trajectory prediction model, and we built 4 cases simulation environments, for two or three vehicles on a straight road or curved road, respectively, to test the decision-making model. Experimental results show that our proposed model can ensure driving safety and improve driving comfort.

This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle’s driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.