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Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

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).

For an electric vehicle driven by four in-wheel motors, the torque of each wheel can be controlled precisely and independently. A closed-loop control method of differential drive assisted steering (DDAS) has been proposed to improve vehicle steering properties based on those advantages. With consideration of acceleration requirement, a three dimensional characteristic curve that indicates the relation between torque and angle of the steering wheel at different vehicle speeds was designed as a basis of the control system. In order to deal with the saturation of motor's output torque under certain conditions, an anti-windup PI control algorithm was designed. Simulations and vehicle tests, including pivot steering test, lemniscate test and central steering test were carried out to verify the performance of the DDAS in steering portability and road feeling.

Real-time interaction between a driver and the simulator is problematic. In this study, the racing car driving simulator has been established, which is composed of the following functional components: Motion Controller, Simview, Scenario Editor, Application Programmer Interface (APIs) and Crash Simulation. With TCP/IP protocol, the Motion Controller receives driver's manipulation, road unevenness and crash situation of Simview, then generates motion streams that reflecting the current conditions, and sends them to Simview and to the hydraulic platform. Furthermore, by detecting and analyzing general vehicle two-dimensional impact, a kind of complete and applicable calculation method has been established, and complicated vehicle impacts can be analyzed accurately. This racecar driving simulator places a racing driver in a interactive environment, and provides the driver with high-fidelity motion, visual, auditory, and force feedback cues.

Biomechanics and biodynamics are increasingly focused on the automotive industry to provide comfortable driving environment, reduce driver fatigue, and improve passenger safety. Man-centered conception is a growing emphasis on the open design of automobile. During the long-term driving, occupational drivers are easily exposed to the neck pain, so it is important to reduce the muscle force load and its fatigue, which are not usually considered quantitatively during traditional ergonomics design, so standards related are not well developed to guide the vehicle design; On the other hand, the head-neck models are always built based on the statics theory, these are not sufficient to predict the instantaneous variation of the muscle force. In this paper, a head-neck model with multi DOFs is created based on multibody dynamics. Firstly, a driver-vehicle-road model considering driver multi-rigid body model, vehicle subsystems, and different ranks of pavement is built.

Beamforming techniques are widely used today in aeroacoustic wind tunnels to identify wind noise sources generated by interaction between incoming flow and the test object. In this study, a planar spiral microphone array with 120 channels was set out-of-flow at 1:1 aeroacoustic wind tunnel of Shanghai Automotive Wind Tunnel Center (SAWTC) to test exterior wind noise sources of a production car. Simultaneously, 2 reference microphones were set in vehicle interior to record potential sound source signal near the left side view mirror triangle and the signal of driver’s ear position synchronously. In addition, a spherical array with 48 channels was set inside the vehicle to identify interior noise sources synchronously as well. With different correlation methods and an advanced algorithm CLEAN-SC, the ranking of contributions of vehicle exterior wind noise sources to interested interior noise locations was accomplished.

With the development of automotive HMI and mobile internet, many interactive modes are available for drivers to fulfill the in-vehicle secondary tasks, e.g. dialing, volume adjustment, music playing. For driving safety and drivers’ high expectation for HMI, it is urgent to effectively evaluate interactive mode with good efficiency, safety and good user experience for each secondary tasks, e.g. steering wheel buttons, voice control. This study uses a static driving simulation cockpit to provide driving environment, and sets up a high-fidelity driving cockpit based on OKTAL SacnerStudio and three-dimensional modeling technology. The secondary tasks supported by HMI platform are designed by customer demands research. The secondary task test is carried out based on usability test theory, and the influence on driving safety by different interactive modes is analyzed.

Based on the emergency lane change cases extracted from the China naturalistic driving data, the driving steering behavior divides into three phases: collision avoidance, lateral movement and steering stabilization. Using the steering primitive fitting by Gaussian function, the distribution of the duration time, the relationship between steering wheel rate and deflection were analyzed in three phases. It is shown that the steering behavior essentially is composed of steering primitives during the emergency lane-change. However, the combination of the steering primitives is different according to the specific steering constraints in three phases. In the collision avoidance phase, a single steering primitive with high peak is used for the fast steering; in the lateral movement and stabilization phase, a combination of two or even more steering primitives is built to a more accurate steering.

Driving posture study is essential for the evaluation of the occupant packaging. This paper presents a method of reconstructing driver’s postures in a real vehicle using a 3D laser scanner and Human Builder (HB), the digital human modeling tool under CATIA. The scanning data was at first converted into the format readable by CATIA, and then a personalized HB manikin was generated mainly using stature, sitting height and weight. Its pelvis position and joint angles were manually adjusted so as to match the manikin with the scan envelop. If needed, a fine adjustment of some anthropometric dimensions was also preceded. Finally the personalized manikin was put in the vehicle coordinate system, and joint angels and joint positions were extracted for further analysis.

X-in-the-loop (XiL) framework is a validation concept for vehicle product development, which integrates different virtual and physical components to improve the development efficiency. In order to develop and validate an extended validation method based on XiL, Tongji University in Shanghai, China and the Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany co- performed a feasibility study about an X-in-the-distance-loop demonstration platform. The X-in-the-distance-loop demonstration platform includes a MATLAB/Simulink software platform and geographically distributed equipment (driver simulator, driving electric motor and dynamometer test stand), which are used to conduct bidirectional experiments to test communication of powertrain data between China and Germany.

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.

With the introduction of BroadR-Reach and time-sensitive networking (TSN), Ethernet has become an option for in-vehicle networks (IVNs). Although it has been used in the IT field for decades, it is a new technology for automotive, and thus requires extensive testing. Current test solutions usually target specifications rather than the in-vehicle environment, which means that some properties are still uncertain for in-vehicle usage (e.g., fault tolerance for shorted or open wires). However, these characteristics must be cleared before applying Ethernet in IVNs, because of stringent vehicular safety requirements. Because CAN is usually used for these environments, automotive Ethernet is expected to have the same or better level of fault tolerance. Both CAN and BroadR-Reach use a single pair of twisted wires for physical media; thus, the traditional fault-tolerance test method can be applied for automotive Ethernet.

The integrated control system which combines the differential drive assisted steering (DDAS) and the direct yaw moment control (DYC) for the distributed drive electric vehicle (DDEV) is studied. A handling improvement algorithm for the normal cornering maneuvers is proposed based on motion tracking control. Considering the ideal assistant power character curves at different velocities, an open-loop DDAS control strategy is developed to respond the driver’s demand of steering wheel torque. The DYC strategy contains the steering angle feedforward and the yaw rate feedback. The steering angle feedforward control strategy is employed to improve yaw rate steady gain of vehicle. The maximum feedforward coefficients at different velocities are obtained from the constraint of the motor external characteristic, final feedforward coefficients are calculated according to the ideal assistant power character curve of the DDAS.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

For a practical pad design, a magnetic shielding layer is imperative which is made of ferrite, aluminum or some other metallic material. However, once the magnetic shielding layer is added, not only the mutual inductance but also the self-inductance of the coupling coils vary with the lateral misalignment which is inevitable for a human driver. The change of self-inductance will also result in the mistuning problem in the resonant circuit, which can significantly reduce the transmission efficiency of the whole wireless power transfer (WPT) system. This paper proposed a method of parameter identification of self-inductance based on the least square in order to solve the mistuning problem. In order to verify the proposed method, both the simulation model and the experiment set-up are built.

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.

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

The driver monitoring system (DMS) plays an essential role in reducing traffic accidents caused by human errors due to driver distraction and fatigue. The vision-based DMS has been the most widely used because of its advantages of non-contact and high recognition accuracy. However, the traditional RGB camera-based DMS has poor recognition accuracy under complex lighting conditions, while the IR-based DMS has a high cost. In order to improve the recognition accuracy of conventional RGB camera-based DMS under complicated illumination conditions, this paper proposes a lightweight low-illumination image enhancement network inspired by the Retinex theory. The lightweight aspect of the network structure is realized by introducing a pixel-wise adjustment function. In addition, the optimization bottleneck problem is solved by introducing the shortcut mechanism.