Bo Cheng

Eco-Departure of Connected Vehicles With V2X Communication at Signalized Intersections

Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper.

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

The recent progress of advanced vehicle control systems presents a great opportunity for the application of model predictive control (MPC) in the automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve real-time implementation. This paper presents a generic scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is formulated by combining an existing “move blocking ” strategy with a “constraint-set compression” strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon. The closed-loop stability is guaranteed by adding terminal zero-state constraint. The tradeoff between control optimality and computational intensity is achieved by proper design of the blocking and compression matrices. The fast algorithm has been applied on intelligent vehicular longitudinal automation, implemented as a fuel economy-oriented adaptive cruise controller and experimentally evaluated by a series of real-time simulations and field tests. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.

Detection of Driver Cognitive Distraction: A Comparison Study of Stop-Controlled Intersection and Speed-Limited Highway

Driver distraction has been identified as one major cause of unsafe driving. The existing studies on cognitive distraction detection mainly focused on high-speed driving situations, but less on low-speed traffic in urban driving. This paper presents a method for the detection of driver cognitive distraction at stop-controlled intersections and compares its feature subsets and classification accuracy with that on a speed-limited highway. In the simulator study, 27 subjects were recruited to participate. Driver cognitive distraction is induced by the clock task that taxes visuospatial working memory. The support vector machine (SVM) recursive feature elimination algorithm is used to extract an optimal feature subset out of features constructed from driving performance and eye movement. After feature extraction, the SVM classifier is trained and cross-validated within subjects. On average, the classifier based on the fusion of driving performance and eye movement yields the best correct rate and F-measure (correctrate = 95.8 ± 4.4%; for stop-controlled intersections and correct rate = 93.7 ± 5.0%; for a speed-limited highway) among four types of the SVM model based on different candidate features. The comparisons of extracted optimal feature subsets and the SVM performance between two typical driving scenarios are presented.

Fuel-Saving Cruising Strategies for Parallel HEVs

This paper studies the fuel-optimal cruising strategies of parallel hybrid electric vehicles (HEVs) and their underlying mechanisms. To achieve fuel-optimal operations, a discontinuous nonlinear optimal control problem is formulated and solved using the Legendre pseudospectral method and the knotting technique. Three optimal cruising strategies in free/fixed-speed cruising scenarios are proposed: vehicle speed pulse-and-glide strategy, state-of-charge (SoC) pulse-and-glide (PnG) strategy, and constant-speed strategy. The performance and optimal behavior of the engine and the motor are presented, and their fuel-saving mechanisms are explained. Finally, two principles to compromise between fuel economy and ride comfort are proposed and studied.

Fuel-Optimal Cruising Strategy for Road Vehicles With Step-Gear Mechanical Transmission

This paper studies the principles and mechanism of a fuel-optimal strategy in cruising scenarios, i.e., the pulse and glide (PnG) operation, for road vehicles equipped with a step-gear transmission. In the PnG strategy, the control of the engine and the transmission determines the fuel-saving performance, and it is obtained by solving an optimal control problem (OCP). Due to a discrete gear ratio, strong nonlinear engine fuel characteristics, and different dynamics in the pulse/glide mode, the OCP is a switching nonlinear mixed-integer problem. This challenging problem is converted by a knotting technique and the Legendre pseudospectral method to a nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The optimization results show the significant fuel saving of the PnG operation as compared with the constant-speed cruising strategy. The underlying fuel-saving mechanism of the PnG strategy is explained graphically. For a real-time implementation, a near-optimal practical rule that enables a driver and/or an automatic control system to fast select gear positions and engine torque profile is proposed with only slightly deteriorated fuel saving.

A Unified Pseudospectral Computational Framework for Optimal Control of Road Vehicles

This paper presents a unified pseudospectral computational framework for accurately and efficiently solving optimal control problems (OCPs) of road vehicles. Under this framework, any continuous-time OCP is converted into a nonlinear programming (NLP) problem via pseudospectral transformation, in which both states and controls are approximated by global Lagrange interpolating polynomials at Legendre-Gauss-Lobatto (LGL) collocation points. The mapping relationship between the costates of OCP and the KKT multipliers of NLP is derived for checking the optimality of solutions. For the sake of engineering practice, a quasi-Newton iterative algorithm is integrated to accurately calculate the LGL points, and a multiphase preprocessing strategy is proposed to handle nonsmooth problems. A general solver called pseudospectral OCP solver (POPS) is developed in MATLAB environment to implement the computational framework. Finally, two classic vehicle automation problems are formulated and numerically solved by POPS: 1) optimization of ecodriving strategy in hilly road conditions; and 2) optimal path planning in an overtaking scenario. The comparison with an equally spaced direct method is presented to show the effectiveness of this unified framework.

Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System of Road Vehicles With Step-Gear Transmission

Fuel consumption of fossil-based road vehicles is significantly affected by the way vehicles are driven. The same is true for automated vehicles with longitudinal control. This paper presents a periodic servo-loop longitudinal control algorithm for an adaptive cruise control (ACC) system to minimize fuel consumption in car-following scenarios. The fuel-saving mechanism of pulse-and-glide (PnG) operation is first discussed for the powertrain with internal combustion engine and step-gear transmission. The servo-loop controller is then designed based on a periodic switching map for real-time implementation and adjusted with a range-bounded feedback regulator to enhance the robustness to model mismatch. Simulations in both uniform and natural traffic flows demonstrate that this algorithm achieves a significant fuel-saving benefit in automated car-following scenarios up to 8.9% in naturalistic traffic flow (when coasting at neutral gear), compared with a linear quadratic (LQ) controller. Meanwhile, its intervehicle range is preferably bounded so that the negative impact on safety and traffic smoothness is contained.

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

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) features from fixed sliding windows on real-time steering wheel angles time series. After that, 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.

Lane change maneuver recognition via vehicle state and driver operation signals — Results from naturalistic driving data

Lane change maneuver recognition is critical in driver characteristics analysis and driver behavior modeling for active safety systems. This paper presents an enhanced classification method to recognize lane change maneuver by using optimized features exclusively extracted from vehicle state and driver operation signals. The sequential forward floating selection (SFFS) algorithm was adopted to select the optimized feature set to maximize the k-nearest-neighbor classifier performance. The hidden Markov models (HMMs), based on the optimized feature set, were developed to classify driver lane change and lane keeping maneuvers. Fifteen drivers participated in the road test for validation with an accumulation of 2,200 km naturalistic driving data, from which 372 lane changes were extracted. Results show that the recognition rate of lane change maneuver achieves 88.2%. The numbers are 87.6% and 88.8% for left and right lane change maneuvers, respectively, superior to the results from conventional classifiers.

Legendre pseudospectral computation of optimal speed profiles for vehicle eco-driving system

This paper presents a computational framework to solve optimal control problems (OCPs) using Legendre Pseudospectral (PS) method and its application to obtain eco-driving strategies for ground vehicles. Both control and state variables of OCPs are approximated by Lagrange interpolating polynomials at the Legendre-Gauss-Lobatto (LGL) collocation points. The OCP is converted into a nonlinear programming (NLP) problem, and numerically solved by matured optimization algorithms. To implement the PS method, we developed a computational package, called Pseudospectral Optimal control Problem Solver (POPS) in Matlab environment. Further, the POPS is applied to obtain fuel-optimized driving strategies for automated vehicles in hilly road conditions.