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In this paper, we present a model predictive controller for the autonomous vehicle lane-change maneuver. Firstly, an optimal trajectory is generated by polynomial, then, utilize it as the reference trajectory of the controller. It is well known that vehicle with nonholonomic constraints can not be feedback stabilized through continuously differentiable, time-invariant control laws. One of the advantages of MPC is the ability to handle constraints in a straightforward way. Quadratic programming is used to solve a linear MPC by successive linearization of an error model of the vehicle. Due to that the vehicle dynamics model is used, in order to prevent optimal solution cannot be obtained within the prescribed time, the relaxation factor in the objective function.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

Nowadays, the vehicle market puts forward urgent requirement for new kinds of braking booster because the traditional vacuum booster cannot meet the demands of new energy vehicles anymore. However, one problem that all the new plans should face is how to guarantee an ideal pedal feeling. In this paper, a novel mechatronics braking booster is proposed, and servo motor introduced into the booster makes the assist rate can be adjusted under a great degrees of freedom, so the structural parameters and control parameters of the booster should be determined elaborately to get an optimal pedal feeling. The pedal feeling is always represented by the pedal stoke-force curve which is influenced by different parameters.

When automobiles are at the threat of collisions, steering usually needs shorter longitudinal distance than braking for collision avoidance, especially under the condition of high speed or low adhesion. Thus, more collision accidents can be avoided in the same situation. The steering assistance is in need since the operation is hard for drivers. And considering the dynamic characteristics of vehicles in those maneuvers, the real-time and the accuracy of the assisted algorithms is essential. In view of the above problems, this paper first takes lateral acceleration of the vehicle as the constraint, aiming at the collision avoidance situation of the straight lane and the stable driving inside the curve, and trajectory of the collision avoidance is derived by a quintic polynomial.

A vehicle dynamics stability control system based on integrated-electro-hydraulic brake (I-EHB) system with hierarchical control architecture and nonlinear control method is designed to improve the vehicle dynamics stability under extreme conditions in this paper. The I-EHB system is a novel brake-by-wire system, and is suitable to the development demands of intelligent vehicle technology and new energy vehicle technology. Four inlet valves and four outlet valves are added to the layout of a conventional four-channel hydraulic control unit. A permanent-magnet synchronous motor (PMSM) provides a stabilized high-pressure source in the master cylinder, and the four-channel hydraulic control unit ensures that the pressures in each wheel cylinder can be modulated separately at a high precision. Besides, the functions of Anti-lock Braking System, Traction Control System and Regenerative Braking System, Autonomous Emergency Braking can be integrated in this brake-by-wire system.

Environmental sensing and perception is one of the key technologies on intelligent driving or autonomous vehicles. As a complementary part to current radar and lidar sensors, ultrasonic sensor has become more and more popular due to its high value to the cost. Different from other sensors mainly based on propagation of electromagnetic wave, ultrasonic sensor possesses some unique features and physical characteristics that bring many merits to autonomous vehicle research, like transparent obstacles and highly reflective surfaces detection. Its low-cost property can further bring down hardware cost to foster widespread use of intelligent driving or autonomous vehicles. To accelerate the development of autonomous vehicle, this paper proposes a high fidelity ultrasonic sensor model based on its physical characteristics, including obstacle detection, distance measurement and signal attenuation.

In this paper, a model predictive control (MPC) based trajectory tracking scheme utilizing steering wheel and braking or acceleration pedal is proposed for intelligent vehicles. The control objective is to track a desired trajectory which is obtained from the trajectory planner. The proposed control is based on a simplified third-order vehicle model, which consists of longitudinal vehicle dynamics along with a commonly used bicycle model. A nonlinear model predictive control (NMPC) is adopted in order to follow a given path by controlling front steering, braking and traction, while fulfilling various physical and design constraints. In order to reduce the computational burden, the NMPC is converted to a linear time-varying (LTV) MPC based on successive online linearization of the nonlinear system model. Two different test conditions have been used to verify the effectiveness of the proposed approaches through simulations using Matlab and CarSim.

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

This paper presents a unified novel function-based brake control architecture, which is designed based on a top-down approach with functional abstraction and modularity. The proposed control architecture includes a commands interpreter module, including a driver commands interpreter to interpret driver intention, and a command integration to integrate the driver intention with senor-guided active driving command, state observers for estimation of vehicle sideslip, vehicle speed, tire lateral and longitudinal slips, tire-road friction coefficient, etc., a commands integrated control allocation module which aims to generate braking force and yaw moment commands and provide optimal distribution among four wheels without body instability and wheel lock or slip, a low-level control module includes four wheel pressure control modules, each of which regulates wheel pressure by fast and accurate tracking commanded wheel pressure.

The evaluation of electric vehicle electric/electronic-architectures (e/e-architectures) is the main topic of this paper. The electric vehicle is chosen as an example system, as it reflects the typical challenges of modern vehicle e/e-architecture development. The development of modern automotive technology also presents another important trend - vehicle electrification. New electric and electronic devices are developed and required in the automotive industry and control commands are exchanged by electric and electronic ones. The energy storage systems (ESS) properly reflect the above two aspects. The energy storage device also takes care of the peak loads, the high load dynamics, and it utilizes the braking energy in order to increase the efficiency. In this work a Li-ion battery and an ultracapacitor both are considered as energy storage devices.

This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.

This paper first presents an algorithm to detect tire blowout based on wheel speed sensor signals, which either reduces the cost for a TPMS or provides a backup in case it fails, and a tire blowout model considering different tire pressure is also built based on the UniTire model. The vehicle dynamic model uses commercial software CarSim. After detecting tire blowout, the active braking control, based on a 2DOF reference model, determines an optimal correcting yaw moment and the braking forces that slow down and stop the vehicle, based on a linear quadratic regulator. Then the braking force commands are further translated into target pressure command for each wheel cylinder to ensure the target braking forces are generated. Some simulations are conducted to verify the active control strategy.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.