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With the increasing worldwide concern on environmental pollution, battery electrical vehicles (BEV) have attracted a lot attention. However, it still couldn’t satisfy the market requirements because of the low battery power density, high cost and long charging time. The range-extended electrical vehicle (REEV) got more attention because it could avoid the mileage anxiety of the BEVs with lower cost and potentially higher efficiency. When internal combustion engine (ICE) works as the power source of range extender (RE) for REEV, its NVH, emissions in starting process need to be optimized. In this paper, a 2-cylinder PFI gasoline engine and a permanent magnet synchronous motor (PMSM) are coaxially connected. Meanwhile, batteries and load systems were equipped. The RE co-control system was developed based on Compact RIO (Compact Reconfigurable IO), Labview and motor control unit (MCU).

Multi-sensor information fusion framework is the eyes for unmanned driving and Advanced Driver Assistance System (ADAS) to perceive the surrounding environment. In addition to the perception of the surrounding environment, real-time vehicle localization is also the key and difficult point of unmanned driving technology. The disappearance of high-precision GPS signal suddenly and defect of the lane line will bring much more difficult and dangerous for vehicle localization when the vehicle is on unmanned driving. In this paper, a road boundary feature extraction algorithm is proposed based on multi-sensor information fusion of automotive radar and vision to realize the auxiliary localization of vehicles. Firstly, we designed a 79GHz (78-81GHz) Ultra-Wide Band (UWB) millimeter-wave radar, which can obtain the point cloud information of road boundary features such as guardrail or green belt and so on.

The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.

Electric Power Steering (EPS) is the actuator of several lateral-dynamic-related Advanced Driver Assistance Systems (ADAS). A driving simulator with EPS will be much helpful for the ADAS development. However, if a real EPS is used in the driving simulator, it is quite difficult to realize the road reaction force accurately and responsively. To overcome this weakness, a virtual EPS platform is established. The virtual EPS platform contains two parts: one is the vehicle and EPS model, the other is the force feedback actuator (FFA) of the Steer-by-Wire (SBW) system. The FFA is an interface between the driver and the EPS/vehicle model. The reactive torque of the FFA is obtained based on the models. Meanwhile, the input of the EPS model is the steering angle of the FFA. Comparing to a real EPS, the virtual EPS platform has a problem of instability because of the actuator lag of the FFA. Therefore, a damping control method is applied to make the system stable.

In the recent years, the interaction between human driver and Advanced Driver Assistance System (ADAS) has gradually aroused people’s concern. As a result, the concept of personalized ADAS is being put forward. As an important system of ADAS, Lane Keeping Assistance System (LKAS) also attracts great attention. To achieve personalized LKAS, driver lane keeping characteristic (DLKC) indices which could distinguish different driver lane keeping behavior should be researched. However, there are few researches on DLKC indices for personalized LKAS. Although there are many researches on modeling driver steering behavior, these researches are not sufficient to obtain DLKC indices. One reason is that most of researches are for double lane change behavior which is different from driver lane keeping behavior. The other reason is that the researches on driver lane keeping behavior only provide model structure and rarely discuss identification procedure such as how to select suitable data.

In China there are many mixed driving roads which cause a lot of safety problems between vehicles and pedalcyclists. Research on driver behavior under risk scenarios with pedalcyclist is relatively few. In this paper driver brake parameters under naturalistic driving are studied and pedalcyclists include bicyclist, tricyclist, electric bicyclist and motorcyclist. Brake reaction time and maximum brake jerk are used to evaluate driver brake reaction speed. Average deceleration is used to evaluate the effect of driver brake operation. Maximum deceleration is used to evaluate driver braking ability. Driver behaviors collected in China are classified and risk scenarios with pedalcyclist are obtained. Driver brake parameters are extracted and statistical characteristics of driver brake parameters are obtained. Influence factors are analyzed with univariate ANOVA and regression analysis.

Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of Advanced Driver Assistance Systems (ADAS) and autonomous driving. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on centralized fusion strategy that the fusion center takes a unified track management. At First, vision sensor and radar are used to detect the target and to measure the range and the azimuth angle of the target. Then, the detections data from vision sensor and radar is transferred to fusion center to join the multi-target tracking with the prediction of current tracks.

Cut-in scenario is common in traffic and has potential collision risk. Human driver can detect other vehicle’s cut-in intention and take appropriate maneuvers to reduce collision risk. However, autonomous driving systems don’t have as good performance as human driver. Hence a deeper understanding on driving behavior is necessary. How to make decisions like human driver is an important problem for automated vehicles. In this paper, a method is proposed to classify the dangerous cut-in situations and normal ones. Dangerous cases were extracted automatically from naturalistic driving database using specific detection criteria. Among those cases, 70 valid dangerous cut-in cases were selected manually. The largest deceleration of subject vehicle is over 4 m/s2. Besides, 249 normal cut-in cases were extracted by going through video data of 2000km traveled distance. In normal driving cases, subject vehicle may brake or keep accelerating and the largest deceleration was less than 3 m/s2.

High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object detector-based advanced driver assistance systems (ADAS) and autonomous driving systems. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the motion of the vehicle and the position of objects in the traffic scene, which limits the consistence between the resulting augmented images and traffic scenes. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes to generate more realistic augmented images and achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene.

Lane Keeping Assistance (LKA) system is a very important part in Advanced Driver Assistance Systems (ADAS). It prevents a vehicle from departing out of the lane by exerting intervention. But an inappropriate performance during LKA intervention makes driver feel uncomfortable. The intervention of LKA can be divided into 3 parts: intervention timing, intervention process and intervention ending. Many researches have studied about the intervention timing and ending, but factors during intervention process also affect driver feelings a lot, such as yaw rate and steering wheel velocity. To increase driver’s acceptance of LKA, objective and subjective tests were designed and conducted to explore important indices which are highly correlated with the driver feelings. Different kinds of LKA controller control intervention process in different ways. Therefore, it’s very important to describe the intervention process uniformly and objectively.

Cut-in scenarios are common and of potential risk in China but Advanced Driver Assistant System (ADAS) doesn’t work well under such scenarios. In order to improve the acceptance of ADAS, its reactions to Cut-in scenarios should meet driver’s driving habits and expectancy. Brake is considered as an express of risk and brake tendency in normal Cut-in situations needs more investigation. Under critical Cut-in scenarios, driver tends to brake hard to eliminate collision risk when cutting in vehicle right crossing lane. However, under less critical Cut-in scenarios, namely normal Cut-in scenarios, driver brakes in some cases and takes no brake maneuver in others. The time when driver initiated to brake was defined as key time. If driver had no brake maneuver, the time when cutting-in vehicle right crossed lane was defined as key time. This paper focuses on driver’s brake tendency at key time under normal Cut-in situations.

The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image.

Driver behaviors provide abundant information and feedback for future Advanced Driver Assistance Systems (ADAS). Driver’s eye and head may present some typical movement patterns before executing driving maneuvers. It is possible to use driver’s head and eye movement information for predicting driver intention. Therefore, to determine the most important features related to driver intention has attracted widespread research interests. In this paper, a method to analyze the correlation between driver’s visual features and driver intention is proposed, aiming to determine the most representative features for driver intention prediction. Firstly, naturalistic driving experiment is conducted to collect driver’s videos during executing lane keeping and lane change maneuvers. Then, driver’s head and face visual features are extracted from those videos. By using boxplot and independent samples T-test, features which have significant correlation with driver intention are found.

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.

Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance.

Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. Lidar sensing gives us high-resolution data by sending out thousands of laser signals. In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects.

Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

Aiming at the problems of ineffective collision avoidance and vehicle instability in the process of vehicle emergency braking in road conditions with low adhesion and sudden change in adhesion coefficient, a stability-coordinated emergency braking and collision avoidance control system SEBCACS) is proposed. First, according to the motion of the ego vehicle and the target vehicle as well as the road adhesion conditions, a collision time model is proposed for evaluating the vehicle collision risk, and the expected deceleration required to avoid the collision is calculated. Then, the MPC method is used to calculate the yaw moment generated by the four-wheel braking force required to maintain vehicle stability according to the actual and reference yaw rate and side slip angle deviation. Then it is decided whether to implement additional yaw moment control according to the body stability evaluation results.

Permanent magnet synchronous motors (PMSM) are widely used in the electric vehicles for their high power density and high energy efficiency. And the motor control system for electric vehicles is one of the most critical safety related systems in electric vehicles, because potential failures of this system can lead to serious harm to humans’ body, so normally a high automotive safety integrity level (ASIL) will be assigned to this system. In this paper, an ASIL-C motor control system based on a multicore microcontroller is presented. At the same time, due to the increasing number of connectivity on the vehicle, secure onboard communication conformed to the AUTOSAR standard is also implemented in the system to prevent external attacks.

Automatic Lane Change (ALC) function is an important step to promote the currently popular Advanced Driver Assistance Systems (ADAS) within a single lane. The key issue for ALC is accurate steering angle and trajectory tracking during the lane changing process. In this paper, an MPC controller with a receding horizon is designed to track the desired trajectory. During the tracking process, other objectives such as safety and smoothness are considered. Considering of the practical mechanism and parameter uncertainties, an SMC controller is designed to track the target steering angle. For validation, a Hardware-in-the-Loop (HIL) experiment platform is established, and experiments of different control algorithms under different conditions are carried out successively. Comparisons of the experiment results of MPC+SMC and PID+SMC schemes indicate that both the trajectory error and the steering angle error of the former combination are smaller.