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Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

With the development of intelligent and electric vehicles, higher requirements are put forward for the active braking and regenerative braking ability of the braking system. The traditional braking system equipped with vacuum booster has difficulty meeting the demand, therefore it has gradually been replaced by the integrated braking system. In this paper, a novel Integrated Braking System (IBS) is presented, which mainly contains a pedal feel simulator, a permanent magnet synchronous motor (PMSM), a series of transmission mechanisms, and the hydraulic control unit. As an integrative system of mechanics-electronics-hydraulics, the IBS has complex nonlinear characteristics, which challenge the accurate pressure control. Furthermore, it is a completely decoupled braking system, the pedal force doesn’t participate in pressure-building, so it is necessary to precisely identify driver’s braking intention.

The Electronic Stability Program (ESP), as a key actuator of traditional automobile braking system, plays an important role in the development of intelligent vehicles by accurately controlling the pressure of wheels. However, the ESP is a highly nonlinear controlled object due to the changing of the working temperature, humidity, and hydraulic load. In this paper, an accurate pressure control strategy of single wheel during active braking of ESP is proposed, which doesn’t rely on the specific parameters of the hydraulic system and ESP. First, the structure and working principle of ESP have been introduced. Then, we discuss the possibility of Pulse Width Modulation (PWM) control based on the mathematical model of the high-speed switching valve. Subsequently, the pressure building characteristics of the inlet and outlet valves are analyzed by the hardware in the Loop (HiL) experimental platform.

Adaptive cruise control system with lane change assistance (LCACC) is a novel advanced driver assistance system (ADAS), which enables dual-target tracking, safe lane change, and longitudinal ride comfort. To design the personalized LCACC system, one of the most important prerequisites is to identify the driver’s individualities. This paper presents a real-time driver behavior characteristics identification strategy for LCACC system. Firstly, a driver behavior data acquisition system was established based on the driver-in-the-loop simulator, and the behavior data of different types of drivers were collected under the typical test condition. Then, the driver behavior characteristics factor Ks we proposed, which combined the longitudinal and lateral control behaviors, was used to identify the driver behavior characteristics. And an individual safe inter-vehicle distances field (ISIDF) was established according to the identification results.

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.

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.

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.

To reduce the interference and conflict of human-machine cooperative control, lighten the operation workload of drivers, and improve the friendliness and acceptability of intelligent vehicles, a personalized human-machine cooperative lane-change trajectory tracking control method was proposed. First, a lane-changing driving data acquisition test was carried out to collect different driving behaviors of different drivers and form the data pool for the machine learning method. Two typical driving behaviors from an aggressive driver and a moderate driver are selected to be studied. Then, a control structure combined by feedforward and feedback control based on Long Short Term Memory (LSTM) and model-based optimum control was introduced. LSTM is a machine learning method that has the ability of memory. It is used to capture the lane-changing behaviors of each driver to achieve personalization. For each driver, a specific personalized controller is trained using his driving data.

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 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.

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.