Motion prediction for the leading vehicle is a critical task for connected autonomous vehicles. It provides a method to model the leading-following vehicle behavior and analysis their interactions. ...In this study, a joint time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The proposed method enables a precise and personalized trajectory prediction for the leading vehicle based on limited inter-vehicle communication signals, such as the vehicle speed and acceleration of the front vehicles. Three different driving styles are first recognized based on an unsupervised clustering algorithm, namely, Gaussian Mixture Model (GMM). The GMM generates a specific driving style for each vehicle based on the speed, acceleration, jerk, time, and space headway features of the leading vehicle. The feature importance of driving style recognition is also evaluated based on the Maximal Information Coefficient (MIC) algorithm. Then, a personalized joint time series modeling (JTSM) method based on the Long Short-Term Memory (LSTM) Recurrent Neural Network model (RNN) is proposed to predict the front vehicle trajectories. The JTSM contains a common LSTM layer and different fully connected regression layers for different driving styles. The proposed method is tested with the Next Generation Simulation (NGSIM) data on the US101, and I-80 highway dataset. The JTSM is tested for making predictions one to five seconds ahead. Results indicate that the proposed personalized JTSM approach shows a significant advantage over the baseline algorithms.
It is an effective way to execute a complicated mission by cooperating unmanned vehicles. This paper focuses on a search- and-track (SAT) mission for an underwater target, and the mission is ...implemented by combining an unmanned aerial vehicle (UAV), an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). In the cooperative path planning model, the mission is divided into the search phase and the track phase, and the goals of the two phases are to maximize the search space and minimize the terminal error respectively. The constraints contain the maneuverability of vehicles and communication ranges between vehicles. Strategies based on random simulation experiments and asynchronous planning are developed to design the cooperative path planning algorithm in the two phases, and the paths are generated by an improved particle swarm optimization (IPSO) algorithm in a centralized or a distributed mode. Simulation results demonstrate that the proposed method can deal with different situations. The UAV&USV&AUV system is superior to the USV&AUV system in the SAT mission.
High precision and fast response are of great significance for hydraulic pressure control in automotive braking systems. In this paper, a novel sliding mode control based high-precision hydraulic ...pressure feedback modulation is proposed. Dynamical models of the hydraulic brake system including valve dynamics are established. An open loop load pressure control based on the linear relationship between the pressure-drop and coil current in valve critical open equilibrium state is proposed, and also experimentally validated on a hardware-in-the-loop test rig. The control characteristics under different input pressures and varied coil currents are investigated. Moreover, the sensitivity of the proposed modulation on valve's key structure parameters and environmental temperatures are explored with some unexpected drawbacks. In order to achieve better robustness and precision, a sliding mode control based closed loop scheme is developed for the linear pressure-drop modulation. Comparative tests between this method and the existing methods are carried out. The results validate the effectiveness and superior performance of the proposed closed loop modulation method.
Dynamic state estimation is of considerable importance to the system monitoring, advanced control, and energy management of electrified vehicles (EVs). Among the dynamic states of various vehicle ...systems, the brake pressure is a key state that reflects the braking intent and maneuver of a driver and is highly correlated with the safety and energy performance of an EV. Thus, it is worth formulating a high-precision estimation algorithm for the brake pressure to better identify the braking intent of a driver and further enhance the multiperformance of the EVs. In this article, an integrated time-series model (TSM) based on multivariate deep recurrent neural networks (RNN) with long short-term memory (LSTM) units is developed for the dynamic estimation of the brake pressure of EVs. The naturalistic driving data are collected using a real electric vehicle under standard driving cycle scenarios. The signals of the vehicle and system states are measured using the controller area network (CAN) bus and preprocessed for model training and prediction. Next, a real-time multivariate LSTM-RNN model for brake pressure estimation is constructed based on the integrated speed estimation model. The real-time scheme iteratively estimates the future velocity and integrates this signal with other vehicle states to estimate a precise value of the braking pressure. The proposed integrated TSM approach is compared with several existing baseline methods to demonstrate the advantage of the method. The testing results indicate that the proposed integrated TSM method can achieve a more reliable multistep prediction with a higher accuracy compared to that of the other methods, which demonstrates the feasibility and effectiveness of the proposed approach.
This paper is concerned with combined power-source sizing and energy management optimization for multi-motor-driven electric powertrains. Existing studies focus mostly on adopting heuristically ...determined battery and motor sizes for such powertrains, without a sufficient exploration of the coupling between power-source dimension and energy management strategy. To address this research gap, this paper aims at presenting an alternative, convex programming based method to optimize the multi-power-source integration problem, for vehicular economy maximization. Specifically, for the first time, we leverage this method to optimize an electric bus powertrain configuration with front-and-rear-axle dual motors and a clutch, as a case study. Numerous analysis results, as well as comparisons with common design/control practice, demonstrate the effectiveness and computational benefit of the proposed scheme.
This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based ...framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.
We study optimal reinsurance in the framework of stochastic Stackelberg differential game, in which an insurer and a reinsurer are the two players, and more specifically are considered as the ...follower and the leader of the Stackelberg game, respectively. An optimal reinsurance policy is determined by the Stackelberg equilibrium of the game, consisting of an optimal reinsurance strategy chosen by the insurer and an optimal reinsurance premium strategy by the reinsurer. Both the insurer and the reinsurer aim to maximize their respective mean–variance cost functionals. To overcome the time-inconsistency issue in the game, we formulate the optimization problem of each player as an embedded game and solve it via a corresponding extended Hamilton–Jacobi–Bellman equation. It is found that the Stackelberg equilibrium can be achieved by the pair of a variance reinsurance premium principle and a proportional reinsurance treaty, or that of an expected value reinsurance premium principle and an excess-of-loss reinsurance treaty. Moreover, the former optimal reinsurance policy is determined by a unique, model-free Stackelberg equilibrium; the latter one, though exists, may be non-unique and model-dependent, and depend on the tail behavior of the claim-size distribution to be more specific. Our numerical analysis provides further support for necessity of integrating the insurer and the reinsurer into a unified framework. In this regard, the stochastic Stackelberg differential reinsurance game proposed in this paper is a good candidate to achieve this goal.
The challenging issue of "human-machine copilot" opens up a new frontier to enhancing driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant ...problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver-vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver-IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.
Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep ...convolutional neural networks (CNN) in this paper. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analyzed and discussed.
Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities ...with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.
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