Adapting the energy management strategy (EMS) to a specific route is important for plug-in hybrid electric vehicles (PHEVs), because it is usually preferred to apply a blended strategy to use the electricity and fuel energy during the whole trip to maximize the energy efficiency. Studies show that both the route information and human driving style have great impact on vehicle energy consumption. This brief focuses on developing an EMS for PHEVs running on multiple frequent routes, while the controller may not know the destination of each trip beforehand. The human driving style influence can be learned from his or her historical driving data, and the possible future routes as well as their probabilities can be obtained using global positioning system information and historical driving data. An optimal control problem for the higher level PHEV control is formulated and solved. This EMS can be implemented onboard in real time. Simulation results show that it consumes only 2.5% more energy than the posterior global optimal result in an eight-frequent-route scenario without needing to know the destination of each trip in advance.

Uncontrolled intersections, as a typical urban traffic environment, are challenging scenarios for autonomous driving due to the potential conflicts and lack of co-ordination between traffic participants. Even if both vehicles are rational players, the existence of multiple Nash equilibira in this dynamic game may result in no consensus between them during the interaction. This may lead to unsmooth rides with late emergency brakes. This paper proposes a conflict-aware game theoretic decision-making framework for autonomous vehicles at uncontrolled intersections considering the Nash equilibrium selection. Combining dynamic programming and iterative best response, a dynamic closed-loop Nash equilibrium calculating method is proposed. The issue of multiple equilibria is analyzed. An equilibrium selection mechanism is designed based on sequentially-constructed Conflict-Aware Bimatrix Games (CAB-Games) and the idea of risk dominant equilibria. The proposed policy provides a relatively smooth drive without being too aggressive or conservative. It is demonstrated in simulations and driving simulator experiments that the policy can interact well with other autonomous driving policies as well as human drivers.

The effectiveness of model-driven trajectory generation and control methods for an automated vehicle is closely related to the accuracy of vehicle kinematics and dynamics. However, the dynamic behavior of a vehicle is time-varying and uncertain, especially during high mobility driving. For the data-driven end-to-end trajectory generation and control approach, the transferability and robustness of the trained neural network cannot easily be guaranteed. In this paper, a combined model- and learning-based trajectory generation and control framework for automated overtaking maneuver is presented. Unlike the model-driven approach and end-to-end learning approach, the proposed framework blends the single-track vehicle model of the automated vehicle with time-series driving data to generate safe and high-quality overtaking motions. Specifically, an efficient vectorized implementation of multivariate Gaussian process regression (MGPR) is applied to learn the unmodeled dynamics in hopes of shortening the deviation between the single-track dynamic model and the true vehicle dynamics. A hierarchical nonlinear model predictive control with MGPR is designed for realizing local trajectory generation and control in the overtaking maneuvers. Multi-phases automated overtaking maneuvers at different speed ranges are conducted on roads with different friction coefficients to illustrate the effectiveness of the proposed trajectory generation and control framework.

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle’s sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.