In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the ...internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N 2 ) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.
A combined SOC (State Of Charge) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed. First, the SOC dependency of the nominal parameters of a ...first-order RC (resistor-capacitor) model is determined, and the performance degradation of the nominal model over the battery lifetime is quantified. Second, two Extended Kalman Filters with different time scales are used for combined SOC/SOH monitoring: the SOC is estimated in real-time, and the SOH (the capacity and internal ohmic resistance) is updated offline. The time scale of the SOH estimator is determined based on model accuracy deterioration. The SOC and SOH estimation results are demonstrated by using large amounts of testing data over the battery lifetime.
► Twelve equivalent circuit models for Li-ion batteries are compared.
► Model complexity, accuracy and generalization are comprehensively evaluated.
► The usefulness levels of the models for two ...types of Li-ion cells are studied.
► Multi-swarm particle swarm optimization algorithm is used to parameterize models.
► Battery test system and characterization datasets are depicted.
This paper presents a comparative study of twelve equivalent circuit models for Li-ion batteries. These twelve models were selected from state-of-the-art lumped models reported in the literature. The test data used is obtained from a battery test system with a climate chamber. The test schedule is designed to measure key cell attributes under highly dynamical excitations. The datasets were collected from two types of Li-ion cells under three different temperatures. The multi-swarm particle swarm optimization algorithm is used to identify the optimal model parameters for the two types of Li-ion cells. The usefulness of these models is then studied through a comprehensive evaluation by examining model complexity, model accuracy, and robustness of the model by applying the model to datasets obtained from other cells of the same chemistry type.
This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors ...mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: "wake" and "drowsy". The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the "awake" state, and 15.15% false detections of the "drowsy" state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.
•A CAVs distributed cooperation method for unsignalized intersections is proposed.•Rotation projection is used to transform 2D vehicle clusters to 1D virtual platoons.•A spanning tree is used to ...construct the conflict-free geometry topology.•The algebraic graph theory is used to model the system dynamics.
Connected vehicles will change the modes of future transportation management and organization, especially at intersections. In this paper, we propose a distributed conflict-free cooperation method for multiple connected vehicles at unsignalized intersections. We firstly project the approaching vehicles from different traffic movements into a virtual lane and introduce a conflict-free geometry topology considering the conflict relationship of involved vehicles, thus constructing a virtual platoon. Then we present the modeling of communication topology to describe two modes of information transmission between vehicles. Finally, a distributed controller is designed to stabilize the virtual platoon for conflict-free cooperation at intersections. Numerical simulations validate the effectiveness of this method.
Decision making for self-driving cars is usually tackled by manually encoding rules from drivers’ behaviours or imitating drivers’ manipulation using supervised learning techniques. Both of them rely ...on mass driving data to cover all possible driving scenarios. This study presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labelled driving data. This method comprehensively considers both high-level manoeuvre selection and low-level motion control in both lateral and longitudinal directions. The authors firstly decompose the driving tasks into three manoeuvres, including driving in lane, right lane change and left lane change, and learn the sub-policy for each manoeuvre. Then, a master policy is learned to choose the manoeuvre policy to be executed in the current state. All policies, including master policy and manoeuvre policies, are represented by fully-connected neural networks and trained by using asynchronous parallel reinforcement learners, which builds a mapping from the sensory outputs to driving decisions. Different state spaces and reward functions are designed for each manoeuvre. They apply this method to a highway driving scenario, which demonstrates that it can realise smooth and safe decision making for self-driving cars.
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•Driving behavior can be categorized into multi-maneuvers in different situations.•A framework of driving style analysis is proposed.•Maneuver transition probabilities better identify ...driving style.•High-risk drivers involve more in closing, tailgating, and constrained lane changes.•Transitions of high-risk drivers are more complicated than that of low-risk drivers.
Accurately estimating driving styles is crucial to designing useful driver assistance systems and vehicle control systems for autonomous driving that match how people drive. This paper presents a novel way to identify driving style not in terms of the durations or frequencies of individual maneuver states, but rather the transition patterns between them to see how they are interrelated. Driving behavior in highway traffic was categorized into 12 maneuver states, based on which 144 (12×12) maneuver transition probabilities were obtained. A conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 probabilities. Random forest algorithm was adopted to classify driving styles using the selected features. Results showed that transitions concerning five maneuver states – free driving, approaching, near following, constrained left and right lane changes – could be used to classify driving style reliably. Comparisons with traditional methods were presented and discussed in detail to show that transition probabilities between maneuvers were better at predicting driving style than traditional maneuver frequencies in behavioral analysis.
•A consistent definition of desired inter-vehicle distances is introduced.•Lower-triangular structures of the system matrix are used for stability analysis.•The lower bound of employable time headway ...is derived for string stability.•The lower bound of time headway can be reduced by following more predecessors.
In a platoon of connected vehicles, time headway plays an important role in both traffic capacity and road safety. It is desirable to maintain a lower time headway while satisfying string stability in a platoon, since this leads to a higher traffic capacity and guarantees the disturbance attenuation ability. In this paper, we study a multiple-predecessor following strategy to reduce time headway via vehicle-to-vehicle (V2V) communication. We first introduce a new definition of desired inter-vehicle distances based on the constant time headway (CTH) policy, which is suitable for general communication topologies. By exploiting lower-triangular structures in a time headway matrix and an information topology matrix, we derive a set of necessary and sufficient conditions on feedback gains for internal asymptotic stability. Further, by analyzing the stable region of feedback gains, a necessary and sufficient condition on time headway is also obtained for the string stability specification. It is proved that a platoon can be asymptotically stable and string stable when the time headway is lower bounded. Moreover, this bound can be reduced by increasing the number of predecessors. These results explicitly highlight the benefits of V2V communication on reducing time headway for platooning of connected vehicles.
This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical ...control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.
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