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This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

Taking an off-road vehicle equipped with 32-speed binary logic automatic transmission (AT) as the research object, the slope starting control research is carried out. The slope starting process is divided into the overcoming resistance stage, the sliding friction stage, and the synchronization stage. The control strategies for each stage are designed respectively. Focusing on the control of the sliding friction stage, the equivalent two-speed model of the starting clutch is established, which realizes the calculation of the speed difference and the slip rate between the driving and driven ends of the starting clutch. Furthermore, the slope starting control strategy based on the proportional-integral-derivative (PID) control of the clutch slip rate is designed. Through the simulation tests of the vehicle starting at different slopes, the correctness of the slope starting control strategy has been verified by MATLAB/Simulink.

Aiming at the low transmission efficiency and power density of the hydraulic automatic transmission (AT), and the increasingly complex structure of its planetary gear with the increase of transmission gears, this paper proposes a new type of binary logic transmission (BLT), which adopts the double internal meshing planetary row (DIMPR), based on a heavy-duty commercial vehicle. By introducing the concept of BLT and analyzing the transmission performance of the DIMPR, the process of scheme design of binary double internal meshing planetary gear transmission (BDIMPGT) is established. According to the structural characteristics of the DIMPG, the support structure of the planetary gear is designed based on CAD and CATIA. In the structural design of binary clutches, V-groove clutch parts are coupled to the transmission case, planetary carrier, and sun shaft, respectively, in each DIMPG.

In the field of automatic driving, the combination of 3D LIDAR and inertial measurement unit (IMU) is a common sensor configuration scheme in laser point-cloud localization, high-precision map making and point-cloud target detection. So it is critical to calibrate LIDAR and IMU accurately. At present, due to the large volume and high cost of 3D LIDAR with high-line-number(Such as 64 lines or 128 lines), the configuration scheme of using multiple low-line-number 3D LIDARs appears in the automatic driving vehicle sensing system. However, the common calibration methods are not suitable for multi 3D LIDARs and IMU parameters calibration on autonomous vehicle, which have the disadvantages of cumbersome implementation and low accuracy. In this paper, a joint calibration test platform composed of dual LIDARs and IMU is assembled, and a method of precise automatic calibration based on GPS/RTK data is proposed.

It’s hard to achieve complete self-driving using hand-crafting generalized decision-making rules, while the end-to-end self-driving system is low in complexity, does not require hand-crafting rules, and can deal with complex situations. Modular-based self-driving systems require multi-task fusion and high-precision maps, resulting in high system complexity and increased costs. In end-to-end self-driving, we usually only use camera to obtain scene status information, so image processing is very important. Numerous deep learning applications benefit from multi-task learning, as the multi-task learning can accelerate model training and improve accuracy with combine all tasks into one model, which reduces the amount of calculation and allows these systems to run in real-time. Therefore, the approach of obtaining rich scene state information based on multi-task learning is very attractive. In this paper, we propose an approach to multi-task learning for semantics, geometry and motion.

In order to improve the mode switching performance of parallel hybrid electric vehicles (PHEV) and make better use of the dynamics of the vehicle, this paper proposes a three-stage control method for the start-up mode of start-up, speed synchronization, and clutch slip based on the response characteristics of actual vehicle components and the complex working conditions of the actual road. In the speed synchronization phase, a coordinated control method of “engine speed active following + continuously variable transmission (CVT) speed ratio motor speed limiting” is proposed. The real vehicle test results show that the engine starting-up coordinated control method can significantly accelerate the speed synchronization and shorten the starting-up mode duration during the rapid acceleration, so that the vehicle’s power performance can be well played and the ride comfort can be effectively guaranteed.

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.

With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance. This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object.

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.

The thermal management system of the water medium retarder using engine coolant (water and ethylene glycol) as transmission medium, omits oil-water heat exchanger in the structure. When the hydraulic retarder is operated, the valve is connected with the retarder and water pump, and then the engine coolant enters the working chamber. The kinetic energy of the vehicle is converted into internal energy of the coolant, and the heat is discharged to the external environment through the engine thermal management system. The braking torque of the water medium hydraulic retarder is determined by the water medium flow rate in the working chamber. The smaller the valve opening degree, the greater the braking torque and the faster the heating transmission fluid. Small valve opening is not conducive to the loss of heat. It will affect the normal working of the engine and hydraulic retarder.

The mechanical properties of sandy road are quite different from those of hard surface road. For vehicle control systems such as EMS (engine management system), TCU (transmission control unit) and ABS (antilock brake system), the strategies and parameters set for solid surface road are not optimal for driving on sandy road. It is an effective way to improve the mobility of all-terrain vehicles by identifying sandy road online and shifting the control strategies and parameters of control systems to sandy sets. In this paper, a sandy road identification algorithm for SUVs is proposed. Firstly, the vehicle signals, such as engine torque and speed, gear position, wheel and vehicle speed, are acquired from EMS, TCU and ESP (electronic stability program) through CAN (controller area network) bus respectively. Based on the information and longitudinal force equilibrium equation, the travelling resistance of vehicle is estimated.

Improvement of shift quality evaluation has become more prevalent over the past few years in the development of automatic transmission electronic control system. For the problems of the subjective shift quality evaluation that subjectivity is too strong, the standard cannot be unified and the definition of the objective evaluation index is not clear at present, this paper studies on the methods of objective evaluation of shift quality based on the multi-hierarchical grey relational analysis. Firstly, objective evaluation index system is constructed based on physical quantities, such as the engine speed, the longitudinal acceleration of the vehicle and so on, which broadens the scope of the traditional objective evaluation index further.

Starting control has become a troublesome issue in the developing field of the control system for heavy-duty trucks, due to the complexity of vehicle driving and the variability of driver's intention. The too fast clutch engagement may result in serious impact, influence on the comfort and fatigue life, and even the engine flameout, while the too slow clutch engagement may lead to long time of friction, the increased temperature, and accelerated wear of friction pair, as well as influence on the power performance and fatigue life[1]. Therefore, the key technique of starting control is clutch engagement control, for which the fuzzy PID based optimization of starting control for AMT clutch is proposed, with the pneumatic AMT clutch of heavy-duty trucks as the research object.

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.

Hydraulic retarder, as an auxiliary braking device, is widely used in commercial vehicles. Nowadays, the hydraulic retarder’s internal oil is mainly cooled by the coolant circuit directly. It not only aggravates the load of engine cooling system, but also makes the abundant heat energy not be recycled properly. In this study, an independent energy supply device with organic Rankine cycles is applied to solve the problems above. In the structure of this energy supply device, the evaporator’s inlet and outlet is connected in parallel with the oil outlet and inlet of the retarder respectively. A part of oil enters the evaporator to transfer heat with the organic fluid, and the rest of oil enters the oil-water heat exchanger to be cooled by the coolant circuit. According to the different braking conditions of the retarder, the oil temperature in the inlet of the hydraulic retarder can be kept within the proper range through adjusting the oil flow rate into the evaporator properly.

To improve traditional heavy commercial vehicles performance, this paper introduces a novel hydraulic hub-motor auxiliary system, which could achieve auxiliary driving and auxiliary braking function. Firstly, the system configuration and operation modes are described. In order to achieve coordinating control and distribution of the engine power between mechanical and hydraulic paths, the paper proposes an optimal algorithm based on enhance of vehicle slip efficiency and the results show that displacement of hydraulic variable pump relates with the transmission gear ratio. And then the hydraulic pump displacement controller is designed, in which the feedforward and feedback strategy is adopted. Considering the characteristics of hydraulic hub-motor auxiliary system, a layered auxiliary drive control strategy is proposed in the paper, which includes signal layers, core control layers and executive layers.

A novel torque-coupling architecture for hybrid electric vehicles is proposed. The torque-coupling device is based on automated manual transmission (AMT), which is highly efficient and provides six gears for the engine and three gears for each motor to enable the engine and the motors to work at high-efficiency levels in most cases. The proposed power-shift AMT (P-AMT) does not have a hydraulic torque converter and wet clutches, which dampen the driveline shock. Thus, the drivability control of the P-AMT becomes a challenging issue. Accurate engine, motor model and transmission model have been built and the dynamic control of the gear shift process of PAMT in hybrid mode is simulated. The electric motors compensate for the traction loss during the gear shift of the engine.

In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

In order to improve the drivability and reduce the clutch friction loss, low-cost slope sensor is used in hill-start control of AMT vehicles. After the power spectrum analysis of the original signal and the design of the digital filter, the angle of the slope is obtained with short enough delay and small enough noise. By using this slope angle information, slope resistance force can be calculated online so that the vehicle can be prevented from sliding backward and optimal launch control can be realized. The digital filter of slope angle signal and the optimal controller of dry clutch engagement are embedded in the TCU (Transmission Control Unit) of a micro-car Geely Panda. Real-vehicle experiments are carried out with optimal clutch controller, which shows that the hill-start with low-cost slope sensor and optimal clutch controller can provide successful vehicle launch with little driveline shock. In addition, it can also avoid backward sliding and engine over-speed effectively.