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For intelligent vehicles, a fast and accurate estimation of road slope is of great significance for many aspects, including the steering comfort, fuel economy, vehicle stability control, driving decision-making, etc. But the commonly used estimation methods nowadays usually demand additional sensors or complex dynamic models, causing increase in system complexity as well as decrease in accuracy. To solve these problems, this paper puts forward a real-time road slope estimation algorithm leveraging the relationship between pitch angle and road slope, which only requires low sensors cost and computational complexity. Firstly, a GNSS/INS fusion system is established to obtain the pitch angle with respect to the navigation frame, which couples the vehicle’s pitch angle in vehicle frame and road slope angle.

With the development of brake-by-wire technology, electro-hydraulic composite braking technology came into being. This technology distributes the total braking force demand into motor regenerative braking force and hydraulic braking force, and can achieve a high energy recovery rate. The existing composite braking control belongs to single-channel control, i.e., the four wheel braking pressures are always the same, so the hydraulic braking force distribution relationship of the front and rear wheels does not change. For single-axle-driven electric vehicles, the additional regenerative braking force on the driven wheels will destroy the original braking force distribution relationship, resulting in reduced braking efficiency of the driven wheels, which are much easier to lock under poor road adhesion conditions.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

As one of the essential parts of automatic parking system (APS), the parking motion control module directly affects the system performance and driver experience. Therefore, it is necessary to design a simple, robust and efficient trajectory tracking algorithm which adapt to the various parking conditions. Firstly, considering the predictability and the ability of dealing with various system constraints, the model predictive control (MPC) lateral controller is designed. Then, the second lateral controller based on linear quadratic regulator (LQR) algorithm is designed, which has the excellent capability of balancing the multiple performances of the system. Finally, Stanley lateral controller is designed as the benchmark for horizontal comparison. Parallel and vertical parking simulation environments are proposed to verify the effectiveness of the designed lateral controllers, and the advantages and shortcomings of each control algorithm are horizontally analyzed and summarized.

Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

This paper proposed a lane-change planning method for autonomous vehicle, aiming at fast obstacles avoidance in a way that make smooth and comfortable. The panning algorithm consists of dynamic programming and closed-loop forward simulation. The dynamic programming (DP) was employed to fast search a reference trajectory that avoids obstacles in topological configure space. And the closed-loop forward simulation (CFS) was used to track the reference trajectory for generating smooth trajectory, since the CFS being able to incorporate any nonlinear law and nonlinear vehicle constraints. Furthermore, an anti-windup lateral controller was designed to make the closed-loop forward simulation robust, as the controller being proved to be stable by Lyapunov function. Finally, the numerical results are provided to illustrate the effectiveness of the proposed method.

Since data processing methods could not completely eliminate the uncertainty of signals, it is a key issue for stable and robust decision-making for uncertainty tolerance of intelligent vehicles. In this paper, a decision-making for an Autonomous Emergency Braking (AEB) case considering the uncertainty of road adhesion coefficient estimation (RACE) is proposed. Firstly, the 3σ criterion is employed to classify the confidence in order to establish the decision-making mechanism considering the signal uncertainty of RACE. Secondly, the model for AEB with the uncertainty of the road adhesion coefficient estimated is designed based on the Seungwuk Moon model. Thirdly, a CCRs and CCRm scenario was designed to verify the feasibility in reference to the European New Car Assessment Programme (Euro NCAP) standard. Finally, the results of 10,000 cycles test illustrate that the proposed method is stable and could significantly improve the safety confidence both in the CCRs and CCRm scenarios.

Recently, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure build-up and continuous pressure regulation. In contrast to the vacuum booster, the pressure estimation for EHB is worth to be studied due to its abundant resource (i.e. electric motor) and cost-effective benefit. This work improves an interconnected pressure estimation algorithm (IPEA) based on actuator characteristics by introducing the vehicle dynamics and validates it via vehicle tests. Considering the previous IPEA as the prior pressure estimation, the wheel speed feedback is used for modification via a proportional-integral (PI) observer. Superior to the IPEA based on actuator characteristics, the proposed PEA improves the accuracy by more than 20% under the mismatch of pressure-position relation.

Accurate and fast positioning of large aircraft component is of great importance for Automated Alignment System. The Ball joint is a widely-used mechanical device connecting the aircraft component and positioners. However, there are some shortcomings for the device in man-machine engineering, such as the entry state of the ball-head still needs to be confirmed by the workers and then switched to the locking state manually. To solve above problems, a new positioning mechanism is present in this paper, which consists of a ball-head and a ball-socket. The new device is equipped with a monocular vision system, in which a calibrated industrial camera is used to collect the images of the ball-head. And then, the 3-D coordinate of the ball-head center is calculated by a designed algorithm, guiding the positioner to capture the ball-head. Once the ball-head gets into the ball-socket, the pneumatic system will drive the pistons to move to the specified location.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.

This paper presents several pressure estimation algorithms (PEAs) for a decoupled electro-hydraulic brake system (EHB), which is driven by an electric motor + reduction gear. Most of the pressure control solutions are based on standard pressure-based feedback control, requiring a pressure signal. Although the pressure sensor can produce the pressure feedback signal, it will increase cost and enlarge installation space. The rotation angle of electric motor is available by the built-in sensor, so the pressure can be estimated by using the rotation angle. Considering the typical nonlinearities (i.e. friction, pressure-position relationship) and uncertainties (i.e. disturbance caused by friction model), the estimation-oriented model is established. The LuGre model is selected to describe the friction, and the pressure-position relationship is fitted by a quadratic polynomial.

In the hybrid brake system of electric vehicle, due to the limitation of the motor braking force when the motor is at high speed and the failure of the regenerative braking force when the motor is at low speed, there are three transitional conditions in hybrid braking: the hydraulic brake system intervenes the braking, the hydraulic brake system withdraws the braking and the regenerative braking force withdraws the braking. Due to the response speed of the hydraulic system is slower than that of the motor, there is a large braking impact (the derivative of braking deceleration) in the transitional conditions of hybrid braking, which deteriorates the smoothness and comfort in braking. Aiming at the impact caused by the poor cooperation between the hydraulic braking force and the motor braking force, a coordinated strategy of double closed-loop feedback and motor force correction is proposed in this paper.

Nowadays, SC is recognized as a key element of hybrid energy storage system in modern energy supply chain for electric vehicles (EVs). Co9S8 as a promising electrode material attracts much attention for supercapacitor owing to its superior electrochemical capacity. However, its poor stability and electronic conductivity, which result in inferior cycling performance and rate capability, have seriously limited the practical application of Co9O8 in supercapacitors. In this article, Co9S8 nanoparticles were embedded in reduced graphene oxide (rGO) via a simple anneal approach as high efficient and stable electrodes for SCs. The Co9S8/rGO composites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The Co9S8 nanoparticles were inserted tightly between the rGO layers due to strong intermolecular forces, preventing the cluster in reduction process of rGO from graphene oxide (GO).

Vehicle sideslip angle is significant for electronic stability control devices and hard to estimate due to the nonlinear and uncertain vehicle and tire dynamics. In this paper, based on the two track vehicle dynamic model considering the tire pneumatic trail variation, the vehicle sideslip angle estimation method was proposed. First, the extra steering angle of each wheel caused by kinematics and compliance characteristics of the steering system and suspension system was analyzed. The steering angle estimation method was designed. Since the pneumatic trail would vary with different tire slip angle, distances between the center of gravity (COG) and front&rear axle also change with the tire slip angle. Then, based on the dynamic pneumatic trail and estimated steering angle, we modified the traditional two track vehicle dynamic model using a brush tire model. This model matches the vehicle dynamics more accurately.

Vehicle sideslip angle estimation is of great importance to the vehicle stability control as it could not be measured directly by ordinary vehicle-mounted sensors. As a result, researchers worldwide have carried out comprehensive research in estimating the vehicle sideslip angle. First, as the attitude would affect the acceleration information measured by the IMU directly, different kinds of vehicle attitude estimation methods with multi-sensor fusion are presented. Then, the estimation algorithms of the vehicle sideslip angle are classified into the following three aspects: kinematic model based method, dynamic model based method, and fusion method. The characteristics of different estimation algorithms are also discussed. Finally, the conclusion and development trend of the sideslip angle estimation are prospected.

The integrated control system which combines the differential drive assisted steering (DDAS) and the direct yaw moment control (DYC) for the distributed drive electric vehicle (DDEV) is studied. A handling improvement algorithm for the normal cornering maneuvers is proposed based on motion tracking control. Considering the ideal assistant power character curves at different velocities, an open-loop DDAS control strategy is developed to respond the driver’s demand of steering wheel torque. The DYC strategy contains the steering angle feedforward and the yaw rate feedback. The steering angle feedforward control strategy is employed to improve yaw rate steady gain of vehicle. The maximum feedforward coefficients at different velocities are obtained from the constraint of the motor external characteristic, final feedforward coefficients are calculated according to the ideal assistant power character curve of the DDAS.