Search Results

Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic, therefore, is critically important for autonomous driving simulation on threat assessment, trajectory planning, etc. Traditionally when modeling traffic, the motion of traffic vehicles is often considered to be deterministic and modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic considering its motion uncertainties, based on Gaussian process (GP).

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

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.

Reliable and safe Redundant Steering System (RSS) equipped with Dual-Winding Permanent Magnet Synchronous Motor (DW-PMSM) is considered an ideal actuator for future autonomous vehicle chassis. The built-in DW-PMSM of the RSS is required to identify various winding’s faults such as disconnection, open circuit, and grounding. When achieving redundant control through winding switching, it is necessary to suppress speed fluctuations during the process of winding switching to ensure angle control precision. In this paper, a steering angle safety control for RSS considering motor winding’s faults is proposed. First, we analyze working principle of RSS. Corresponding steering system model and fault model of DW-PMSM have been established. Next, we design the fault diagnosis and fault tolerance strategy of RSS.

The high popularity of automobiles has led to frequent collisions. According to the latest statistics of the United Nations, about 1.25 million people worldwide die from road traffic accidents each year. In order to improve the safety of vehicles in driving, the active safety system has become a research hotspot of various car companies and research institutions around the world. Among them, the more mature and popular active security system are Forward Collision Warning(FCW) and Autonomous Emergency Braking(AEB). However, the current active safety system is based on traditional sensors such as radar and camera. Therefore, the system itself has many limitations due to the shortage of traditional sensors. Compared to traditional sensors, Vehicle to Everything (V2X) technology has the advantages of richer vehicle parameter information, no perceived blind spots, dynamic prediction of dangerous vehicle status, and no occlusion restriction.

The Electromechanical Brake Booster system (EMBB) integrates active braking and energy recovery and becomes a novel brake-by-wire solution that substitutes the vacuum booster. While the intelligent unmanned vehicle is in unstable state, the EMBB can improve the vehicle yaw stability more quickly and safely. In this paper, a new type of integrated EMBB has been designed, which mainly includes two parts: servo motor unit and hydraulic control unit. Aiming at the dynamic instability problem of intelligent unmanned vehicle, a three-layer vehicle yaw stability control structure including decision layer, distribution layer and execution layer is proposed based on integrated EMBB. Firstly, the decision layer calculates the ideal yaw rate and the side slip angle of the vehicle with the classic 2DOF vehicle dynamics model. The boundary of the stable region is determined by the phase plane method and the additional yaw moment is determined by the feedback PI control algorithm.

As a new brake-by-wire solution, the electro-booster (Ebooster) brake system can work with the electronic stability program (ESP) equipped in the real vehicle to realize various excellent functions such as basic force boosting (BFB), active braking and energy recovery, which is promoting the development of smart vehicles. Among them, the BFB is the function of Ebooster's servo force to assist the driver's brake pedal force establishing high-intensity braking pressure. After the BFB function failure of the Ebooster, it was not possible to provide sufficient brake pressure for the driver's normal braking, and eventually led to traffic accidents. In this paper, a compensation redundancy control strategy based on ESP is proposed for the BFB failure of the self-designed Ebooster.

Adaptive Cruise Control (ACC) includes three modes: cruise control, car following control, and autonomous emergency braking. Among them, the car following control mode is mainly used to manage the speed and vehicle spacing approach the preceding vehicle within the range of smooth acceleration changes. In addition, although the motion information signal of the preceding vehicle can be collected by auxiliary equipment, it is still a random variable and normally regarded as a disturbance to affect the performance of vehicle controller. Therefore, this paper proposed an ACC strategy considering the disturbance of the preceding vehicle and multi-objective optimization.

Electrification and intelligence are the important development directions of vehicle techniques. The Electro-Mechanical Brake Booster (Ebooster) as a brake booster which is powered by a motor, can be used to replace the traditional vacuum booster. Ebooster not only improves the intelligence level of vehicle braking and significantly improves the braking performance, but also adapts to the application in new energy vehicles and facilitates coordinated regenerative braking. However, Ebooster cannot complete pedal decoupling independently. It needs to cooperate with other components to realize pedal decoupling. In this paper, a pedal decoupling control algorithm for regenerative brake, which is based on the coordination control of Ebooster and ESP, is proposed. First, regenerative braking strategy is designed to distribute the hydraulic brake force and regenerative braking force.

This paper describes a novel recognition and classification method of vehicle targets in urban road based on a vehicle-mounted Velodyne HDL64E light detection and ranging (LIDAR) system. The autonomous vehicle will choose different driving strategy according to the surrounding traffic environments to guarantee that the driving is safe, stable and efficient. It is helpful for controller to provide the efficient stagey to know the exact type of vehicle around. So this method concentrates on reorganization and classification the type of vehicle targets so that the controller can provide a safe and efficient driving strategy for autonomous ground vehicles. The approach is targeted at high-speed ground vehicle, so real-time performance of the method plays a critical role. In order to improve the real-time performance, some methods of data preprocessing should be taken to simplify the large-size long-range 3D point clouds.

The Electro-Mechanical Brake Booster system (EMBB) is a kind of novel braking booster system, which integrates active braking, regenerative braking, and other functions. It usually composes of a servo motor and the transmission mechanism. EMBB can greatly meet the development needs of vehicle intelligentization and electrification. During active braking, EMBB is required to respond quickly to the braking request and track the target pressure accurately. However, due to the highly nonlinearity of the hydraulic system and EMBB, traditional control algorithms especially for PID algorithm do not work well for pressure control. And a large amount of calibration work is required when applying PID algorithms to pressure control in engineering.

The Electro-Mechanical Brake Booster (Ebooster) is a critical component of the novel brake system for electric intelligent vehicles. It is independent of engine vacuum source, provides powerful active brake performance and can be combined with electric regenerative braking. In this paper, a brake control algorithm for hydraulic brake system equipped with an Ebooster is proposed. First, the configuration of the Ebooster is introduced and the system model including the permanent magnet synchronous motor (PMSM) and hydraulic brake system is established by Matlab/Simulink. Second, a Four-closed-loop algorithm is introduced for accurate active brake pressure control. Finally, according to the requirement of different brake force, series of simulations are carried out under active braking condition. The results show that the control algorithm introduced in this paper can ensure the brake hydraulic pressure tracking a target value precisely and show a good control performance.

The intelligent and electric vehicles are the future of vehicle technique. The development of intelligent and electric vehicles also promotes new requirements to many traditional chassis subsystems, including traditional braking system equipped with vacuum boosters. The Electro-Mechanical Booster is an applicable substitute of traditional vacuum booster for future intelligent and electric vehicles. It is independent of engine vacuum source, and can be combined with electric regenerative braking. A complete system model is necessary for system analysis and algorithm developing. For this purpose, the modeling of electro-hydraulic braking system is necessary. In this paper, a detailed electro-hydraulic braking system model is studied. The system consists of an electro-mechanical booster and hydraulic braking system. The electro-mechanical booster which mainly contains a permanent magnet synchronous motor (PMSM) and a set of transmission mechanism is the critical component.

As bottom layer actuator for the AEB system, the active brake system and the brake force control of tractor-semitrailer have been the hot topics recently. In this paper, a set of active pneumatic brake system was designed based on the traditional brake system of tractor-semitrailer, which can realize the active brake of the vehicle under necessary conditions. Then, a precise mathematical model of the active pneumatic brake system was built by referring the flow characteristics of the solenoid valve, and some tests were implemented to verify the accuracy and validity of the active brake system model. Based on the model, an active pneumatic brake pressure control strategy combining the feedforward and feedback controlling modes was designed. By generating the PWM control signal, it can precisely control the desired wheel cylinder brake pressure of the active brake system. Finally, the brake pressure control strategy was validated both by simulation tests and bench tests.

This paper reports an effort to improve plan of vehicle trajectory using an approach with rapidly-exploring random trees (RRT), which has been widely adopted in the prior art for complex and dynamic traffic environment. Design and implement of an integrated threat assessment is presented that evaluates threats of the trajectory. A node and trajectory evaluation index was introduced into the proposed RRT algorithm to connect an appropriate node and select the best trajectory. The contribution of this paper is on the threat assessment that takes into account not only obstacle avoidance but also stability. The simulation is conducted and the results show that the proposed method works as expected and is valid and effective.

The longitudinal dynamics control is an essential task of vehicle dynamics control. In present, it is usually applied by adjusting the slip ratio of driving wheels to achieve satisfactory performances both in stability and accelerating ability. In order to improve its performances, the coordination of different subsystems such as engine, transmission and braking system has to be considered. In addition, the proposed algorithms usually adopt the threshold methods based on less road condition information for simpleness and quick response, which cannot achieve optimal performance on various road conditions. In this paper, an integrated longitudinal vehicle dynamics control algorithm with tire/road friction estimation was proposed. First, a road identification algorithm was designed to estimate tire forces of driving wheels and the friction coefficient by the method of Kalman Filter and Recursive Least Squares (RLS).

Driver individualities is crucial for the development of the Advanced Driver Assistant System (ADAS). Due to the mechanism that specific driving operation action of individual driver under typical conditions is convergent and differentiated, a novel driver individualities recognition method is constructed in this paper using random forest model. A driver behavior data acquisition system was built using dSPACE real-time simulation platform. Based on that, the driving data of the tested drivers were collected in real time. Then, we extracted main driving data by principal component analysis method. The fuzzy clustering analysis was carried out on the main driving data, and the fuzzy matrix was constructed according to the intrinsic attribute of the driving data. The drivers’ driving data were divided into multiple clusters.

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.

Brake-by-wire (BBW) system has drawn a great attention in recent years as driven by rapidly increasing demands on both active brake controls for intelligent vehicles and regenerative braking controls for electric vehicles. However, unlike conversional brake systems, the reliability of the brake-by-wire systems remains to be challenging due to its lack of physical connection in case of system failure. There are various causes for the failure of a BBW system, such as failure of brake controller, loss of sensor signals, failure of communication or even power supply, to name a few. This paper presents a fault-tolerant control under novel control architecture. The proposed control architecture includes a driver command interpreter module, a command integration module, a control allocation module, a fault diagnosis module and state observers. The fault-tolerant control is designed based on a quadratic optimal control method with consideration of actuator constraints.

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features.