https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, the authors design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design.

/pdf/platoon-based-cooperative-driving-model-with-consideration-1a35j39x3b.pdf

Platoon based Cooperative Driving Model with Consideration of Realistic Inter-vehicle Communication

This paper focuses on the lane-changing trajectory planning (LTP) process in the automatic driving technologies. Existing studies on the LTP algorithms are primarily the static planning method in which the states of the surrounding vehicles of a lane-changing vehicle are assumed to keep unchanged in the whole lane-changing process. However, in real-world traffic, the velocities of the surrounding vehicles change dynamically, and the lane-changing vehicle needs to adjust its velocity and positions correspondingly in real-time to maintain safety. To address such limitations, the dynamic lane-changing trajectory planning (DLTP) model is proposed in the limited literature. This paper proposes a novel DLTP model consisting of the lane-changing starting-point determination module, trajectory decision module and trajectory generation module. The model adopts a time-independent polynomial trajectory curve to avoid the unrealistic assumptions on lane-changing velocities and accelerations in the existing DLTP model. Moreover, a rollover-avoidance algorithm and a collision-avoidance algorithm containing a reaction time are presented to guarantee the lane-changing safety of automated vehicles, even in an emergent braking situation. The field lane-changing data from NGSIM data are used to construct a real traffic environment for lane-changing vehicles and verify the effectiveness of the proposed model, and CarSim is applied to investigate the traceability of the planned lane-changing trajectories using the proposed model. The results indicate that an automated vehicle can complete the lane-changing process smoothly, efficiently and safely following the trajectory planned by the proposed model, and the planned velocity and trajectory can be well-tracked by automated vehicles.

A dynamic lane-changing trajectory planning model for automated vehicles

Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

Automated driving is gaining increasing amounts of attention from both industry and academic communities because it is regarded as the most promising technology for improving road safety in the future. The ability to make an automated lane change is one of the most important parts of automated driving. However, there has been little research into automated lane change maneuvers, and current research has not identified a way to avoid potential collisions during lane changes, which result from the state variations of the other vehicles. One important reason is that the lane change vehicle cannot acquire accurate information regarding the other vehicles, especially the vehicles in the adjacent lane. However, vehicle-to-vehicle communication has the advantage of providing more information, and this information is more accurate than that obtained from other sensors, such as radars and lasers. Therefore, we propose a dynamic automated lane change maneuver based on vehicle-to-vehicle communication to accomplish an automated lane change and eliminate potential collisions during the lane change process. The key technologies for this maneuver are trajectory planning and trajectory tracking. Trajectory planning calculates a reference trajectory satisfying the demands of safety, comfort and traffic efficiency and updates it to avoid potential collisions until the lane change is complete. The trajectory planning method converts the planning problem into a constrained optimization problem using the lane change time and distance. This method is capable of planning a reference trajectory for a normal lane change, an emergency lane change and a change back to the original lane. A trajectory-tracking controller based on sliding mode control calculates the control inputs to make the host vehicle travel along the reference trajectory. Finally, simulations and experiments using a driving simulator are conducted. They demonstrate that the proposed dynamic automated lane change maneuver can avoid potential collisions during the lane change process effectively.

A dynamic automated lane change maneuver based on vehicle-to-vehicle communication

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases

Electric vehicle travel planning method based on multi-target optimization

The invention discloses a pavement peak-value attachment coefficient estimating method on the basis of a UKF (unscented kalman filter) and a corrected Dugoff tire model. The method is characterized by comprising the following steps: collecting each sensor signal of a vehicle in real time, estimating a slip rate and a slip angle of each wheel by utilizing a longitudinal kinetic equation and a geometric coordinate relation of the model; and transmitting the estimated slip rate, vertical force, the slip angle and the like to a UKF coefficient calculation module based on the corrected Dugoff model to obtain a coefficient vector of a nonlinear system, and transmitting the vector and real-time estimated longitudinal force to a UKF pavement peak-value attachment coefficient estimation module to solve a peak-value attachment coefficient. A vehicle state observation system is used for collecting the signals in real time, so that the real-time property of the calculation is guaranteed, and the estimation accuracy on pavement situations which are not fitted is high. By utilizing the corrected Dugoff tire model and a UKF theory, the solving process is simple, the number of operation is small, the rapidness in operation is realized, and the convergence time is short. The method is good in robustness, capable of well recognizing the pavement situations of each wheel and suitable for being used for estimating the pavement peak-value attachment coefficient in real time.

Pavement peak-value attachment coefficient estimating method on basis of UKF (unscented kalman filter) and corrected Dugoff tire model

Advanced electric vehicles equipped with independent electric driving/braking, steering and active suspension systems provide potential for optimal vehicle performance by accurate and independent control of each longitudinal, lateral and vertical tyre force. However, the lack of an effective method for the optimisation and control of tyre forces has restricted the development of this research field. In this paper, a novel method for the coordinated control of longitudinal/lateral/vertical tyre forces is proposed by solving a non-convex optimisation problem. A unified objective function that combines the tyre workload and the dynamic ratio of the vertical forces is developed. Thirty-six constraints, including driving demands, electric actuator characteristics and tyre friction limitations, are considered. An optimisation algorithm that combines constrained optimisation and feasible region planning is proposed. Finally, simulations and experiments using a driver simulator are conducted, which demonstrate that the proposed coordinated control method simultaneously improves handling stability and controls vehicle attitude effectively.

Xiang Yong

Papers

A dynamic automated lane change maneuver based on vehicle-to-vehicle communication

Electric vehicle travel planning method based on multi-target optimization

Pavement peak-value attachment coefficient estimating method on basis of UKF (unscented kalman filter) and corrected Dugoff tire model

Vehicle stability and attitude improvement through the coordinated control of longitudinal, lateral and vertical tyre forces for electric vehicles