Taking driving condition ahead into consideration for proactive power management is one of the most important ways to improve the control performance of hybrid energy storage systems (HESSs). This paper presents a neural network based methodology for power demand prediction and a power distribution strategy for HESSs of pure electric vehicles. To develop an efficient prediction model, driving cycles are first grouped and distinguished as three different driving patterns. For each driving pattern, characteristic parameter data that could better featured driving cycles are extracted effectively and used to train neural network. The predictive information combined with its error is subsequently used for power distribution. Then, to deal with different dynamics of battery and supercapacitor systems, a frequency splitter is used and its frequency is further optimized by a particle swarm optimization algorithm to minimize the total cost including battery degradation and system energy for each driving pattern. Based on these efforts, a real-time predictive power management control strategy is finally proposed. To verify the effectiveness, simulation has been conducted to compare with the state-of-the-art control strategy under a speed profile composing of five standard driving cycles. Results show that obviously enhanced performance can be achieved by the proposed control strategy.

Deep learning techniques have been widely used in autonomous driving community for the purpose of environment perception. Recently, it starts being adopted for learning end-to-end controllers for complex driving scenarios. However, the complexity and nonlinearity of the network architecture limits its interpretability to understand driving scenarios and judge the importance of certain visual regions in sensory scenes. In this paper, based on the convolutional neural network (CNN), we propose two complementary frameworks to automatically determine the most contributive regions of the input scenes, offering intuitive knowledge of how a trained end-to-end autonomous vehicle controller understands driving scenarios. In the first framework, a feature map-based method is proposed by leveraging current progress in CNN visualization, in which the deconvolution approach recovers the feature maps to extract features that contribute most to understand driving scenes. In the second framework, the importance level of regions is ranked using the error map between the labeled and predicted control inputs generated by occluding different parts of input scenes, thus providing a pixel-wise rank of importance. Test data sets with extracted contributive regions are input to the CNN controller. Then, different CNN controllers trained with the new data sets preprocessed using our proposed frameworks are verified via closed-loop tests. Results show that both the features identified from the first framework and the regions identified from the second framework are of crucial importance to scene understanding for the controller and can significantly affect the performance of CNN controllers.

A human-vehicle-environment closed-loop system is the core of the advanced driver-assistance system (ADAS), and the driver is an important part of the closed-loop system. Therefore, an in-depth study on driving habits and personalized driving-assistance systems is conducive to the realization of vehicle safety and intelligent driving. In this paper, we present a personalized vehicle lane-change assistance system integrated with a driver-behavior identification strategy that can select proper occasions and trajectories of lane changes. First, the driver-behavior data-acquisition system is designed and established. Based on this, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions. The drivers are classified utilizing factor analysis and a fuzzy c-means (FCM) clustering algorithm, and the identification of driver behavior is realized using a back-propagation (BP) neural network optimized by a particle swarm optimization (PSO) algorithm. Then, a personalized lane-change warning method based on surrounding multi-vehicle information is proposed. Next, based on a sinusoidal lane-change model, personalized lane-change trajectory planning is proposed, and trajectory tracking is realized by a model predictive control (MPC) algorithm. Finally, a co-simulation is carried out using MATLAB/Simulink and CarSim, and the results show that the lane-change assistance system based on driver behavior can provide personalized occasions and trajectories of lane changes. The proposed trajectory-tracking control algorithm meets the requirements of intelligent vehicle trajectory-tracking control, and has good control effects.

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

Decision-making in uncontrolled intersection is one of the main challenges in urban autonomous driving. This paper proposed a new decision-making framework in uncontrolled intersection based on the intention prediction method and Mixed Strategy Nash Equilibrium theory. The framework is a three-stage method: target vehicle motion prediction, driving mode decision, and motion planning. The driving intention (left turn, right turn, or go straight) of the target vehicle at the intersection can be predicted using the combination algorithm of GMM-HMM and SVM. According to the driving intention and road structure, the trajectory fitting module would use the Bezier curve to fit the predicted trajectory of a target vehicle. Furthermore, combined with trajectories of the ego vehicle, the S-T diagram is used to judge whether there is a spatio-temporal conflict point of the target vehicle and ego vehicle. If there is a conflict point of the target vehicle and ego vehicle, the driving mode (‘yield’ or ‘cross’) of the ego vehicle is selected by using the Mixed Strategy Nash Equilibrium theory. This method can not only avoid premature or unnecessary deceleration due to conservation but also avoid a collision or violent deceleration due to greed. According to the driving mode, the planning module uses the model predictive control algorithm to determine the optimal acceleration strategy. Have been verified by the vehicle test, it indicates that the proposed decision-making framework can make ego vehicles pass through the intersection safely and comfortably.

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow. This, as a result, may impose serious safety risks, and often cause traffic jam with autonomous driving. Therefore, human-like autonomous driving becomes important, yet imperative. This paper presents an interaction-aware decision-making and planning method for human-like autonomous driving in merge scenarios. Rather than directly mimicking human behavior, deep inverse reinforcement learning is employed to learn the human-used reward function for decision-making and planning from naturalistic driving data to enhance interpretability and generalizability. To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles. In contrast to predicting trajectories of traffic vehicles with the fixed trajectory of ADV given by the upstream prediction model, the trajectories of traffic vehicles are predicted by responding to the ADV's behavior in this paper. Additionally, the decision-making module is employed to reduce the solution space of planning via the selection of a proper gap for merging. Both the decision-making and planning algorithms follow a “sampling, evaluation, and selection” framework. After being verified through experiments, the results indicate that the planned trajectories with the presented method are highly similar to those of human drivers. Moreover, compared to the interaction-unaware planning method, the interaction-aware planning method behaves closer to human drivers.