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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.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system. In the existing research, most of the path planning methods use the combined path of arc and straight line. In this method, the path curvature is not continuous, which indirectly leads to the low accuracy of path tracking. The parking path designed using the fifth-order polynomial is continuous, but its curvature is too large to meet the steering constraints in some cases. In this paper, a continuous-curvature parking path is proposed. The parking path tracker based on Model Predictive Control (MPC) algorithm is designed under the constraints of the control accuracy and vehicle steering. Firstly, in order to make the curvature of the parking path continuous, this paper superimposes the fifth-order polynomial with the sigmoid function, and the curve obtained has the continuous and relatively small curvature.

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning and environmental understanding. To precisely extract the road boundaries from the 3D-LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows: Firstly, the 3D-LiDAR data is pre-processed by employing the RANSAC method, the ground points are quickly separated from the original 3D-LiDAR point cloud to reduce the disturbance from the obstacles on the road, this greatly decreases the size of the point cloud to be processed. Secondly, based on the principle of 3D-LiDAR scanning, the ground points are divided into scan layers. And the road boundary points of each scan layer are detected by using three spatial features based on sliding window.

The brake-by-wire system (BBW) is better match the new energy vehicle in the future direction of development. The electro-mechanical brake (EMB) is lack of the brake failure backup and need a high 42 V voltage for the power supply. This paper presents a new brake-by-wire hybrid brake system (HBS) with the electro-hydraulic brake (EHB) equipped on the front wheels and the EMB equipped on the rear wheels. The combination of these two brake-by-wire systems has advantages of both the EHB and EMB system. The EMB on the rear wheels totally removing the rear pipes and can be simply mounted. In addition, since the need of brake torque on the rear axle is relatively small, the power supply of EMB can be reduced to 12 V. Meanwhile, the EHB on the front wheels has the failure backup function through the hydraulic line. The HBS can quickly and accurately regulate four wheels brake force of vehicles which can well meet the requirement of antilock brake system (ABS).

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.

The electric vehicles and the intelligent vehicles put forward to new requirements for the brake system, such as the vacuum-independent braking, automatic or active braking, and regenerative braking, which are the key link for the vehicle’s safety and economy. However, the traditional vacuum brake booster is no longer able to meet these requirements. In this article, a novel integrated power-assisted actuator of brake system is proposed to satisfy the brake system requirements of the electric vehicles and intelligent vehicles. The electronic brake booster system is designed to achieve the function of boosting pedal force of driver, being independent on vacuum source, supplying autonomous or active braking. It is mainly composed of a permanent magnet synchronous motor (PMSM), a two-stage reduction transmission (gears and a ball screw), a servo body, and a reaction disk. The scheme design and power-assisted braking control are the key for the electronic actuator.

As a novel assist actuator of brake system, the electromechanical brake (EMB) booster has played a significant role in the battery electric vehicles and automatic driving vehicles. It has advantages of independent to vacuum source, active braking, and tuning pedal feeling compared with conventional vacuum brake booster. In this article, a novel EMB booster system is proposed, which is consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction by gears and ball screw, a servo body, and a reaction disk. Together with the hydraulic control unit, it has two working modes: active braking for automatic drive and passive braking for driver intervention. The structure and work principle of the electric brake booster system is first introduced. The precise control from pedal force to hydraulic pressure is the key for such a power-assisted brake actuator. We translate the control problem of force feedback control to position tracking control.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.

To meet the new requirements of braking system for modern electrified and intelligent vehicles, various novel electro-mechanical brake boosters (Eboosters) are emerging. This paper is aimed at a new type of the Ebooster, which is mainly consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction transmission and a servo mechanism. Among them, the PMSM is a vital actuator to realize the functions of the Ebooster. To get fast response of the Ebooster system, a novel control strategy employing a maximum torque per ampere (MTPA) control with current compensation decoupling and current-adjusting adaptive flux-weakening control is proposed, which requires the PMSM can operate in a large speed range and maintain a certain anti-load interference capability. Firstly, the wide speed control strategy for the Ebooster’s PMSM is designed in MATLAB/Simulink.

Due to growing interest in automated driving, the need for better understanding of human driving behavior in uncertain environment, such as driving behavior at un-signalized crossroad and roundabout, has further increased. Driving behavior at roundabout is greatly influenced by different dynamic factors such as speed, distance and circulating flow of the potentially conflicting vehicles, and drivers should choose whether to leave or wait at the upcoming exit according to these factors. In this paper, the influential dynamic factors and driving behavior characteristics at the roundabout is analyzed in detail, random forest method is then deployed to predict the driving behavior. For training the driving behavior model, four typical roundabout layouts were created under a real-time driving simulator with PanoSim-RT and dSPACE. Traffic participants with different motion style were also set in the simulation platform to mimic real driving conditions.

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.

When automobile is at the threat of collisions, steering usually needs a shorter longitudinal distance than braking to avoid collision, especially at a high speed. In emergency steering evasion, the vehicle may be out of the road or colliding with obstacles ahead when the driver’s steering torque is excessive or insufficient. In view of the above problems, this paper presents an emergency steering evasion torque assistance system based on optimized trajectory. First, a feasible steering evasion area is established which treats the paths of excessive and insufficient steering as boundary conditions in this paper. An optimized trajectory is derived from the lateral acceleration of the vehicle and the time to the adjacent lane as optimization conditions. Second, a two degree of freedom vehicle model is used to represent dynamics of the vehicle.

Adaptive cruise control (ACC), as one of the advanced driver assistance systems (ADAS), has become increasingly popular in improving both driving safety and comfort. Since the objectives of ACC can be multi-dimensional, and often conflict with each other, it is a challenging task in its control design. The research presented in this paper takes ACC control design as a constrained optimization problem with multiple objectives. A hierarchical framework for ACC control is introduced, aimed to achieve optimal performance on driving safety and comfort, speed and/or distance tracking, and fuel economy whenever possible. Under the hierarchical framework, the operational mode is determined in the upper layer, in which a model predictive control (MPC) based spacing controller is employed to deal with the multiple control objectives. On the other hand, the lower layer is for actuator control, such as braking and driving control for vehicle longitudinal dynamics.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics. The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems. Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

The electro-mechanical brake booster (EMBB) and hydraulic control unit (HCU) constitute the electro-mechanical brake system, which can meet the requirements of brake system for intelligent vehicles. It does not need vacuum source, provides active braking function, have high control accuracy and fast response. But it has two electronic control units (ECU), which need coordinated control. When ABS is triggered, the pressure of the master cylinder keeps rising and falling, and the pressure fluctuates greatly. This will lead to noise and reduce the durability of the system. In this paper, a pressure optimization control strategy under ABS condition is proposed. Firstly, the structure and control strategy of EMBB are introduced. Secondly, the braking characteristics without pressure optimization control are analyzed. Thirdly, based on the demand of maximum cylinder pressure, a three-closed-loop pressure optimization control strategy is established.