The need for a vehicle to perceive information about the external environmental as an independent intelligent individual has grown with the progress of intelligent driving from primary driver assistance to high-level autonomous driving. The ability of a common independent sensing unit to sense the external environment is limited by the sensor’s own characteristics and algorithm level. Hence, a common independent sensing unit fails to obtain comprehensive sensing information independently under conditions such as rain, fog, and night. Accordingly, an extended network-based fusion target detection algorithm for millimeter-wave radar and vision fusion is proposed in this work by combining the complementary perceptual performance of in-vehicle sensing elements, cost effectiveness, and maturity of independent detection technologies. Feature-level fusion is first used in this work according to the analysis of technical routes of the millimeter-wave radar and vision fusion. Training and test evaluation of the algorithm are carried out on the nuScenes dataset and test data from a homemade data acquisition platform. An extended investigation on the RetinaNet one-stage target detection algorithm based on the VGG-16+FPN backbone detection network is then conducted in this work to introduce millimeter-wave radar images as auxiliary information for visual image target detection. We use two-channel radar and three-channel visual images as inputs of the fusion network. We also propose an extended VGG-16 network applicable to millimeter-wave radar and visual fusion and an extended feature pyramid network. Test results showed that the mAP of the proposed network improves by 2.9% and the small target accuracy is enhanced by 18.73% compared with those of the reference network for pure visual image target detection. This finding verified the detection capability and algorithmic feasibility of the proposed extended fusion target detection network for visually insensitive targets.

Vehicle dynamic management (VDM) is a vehicle chassis integrated control system based on electronic stability program (ESP) and continuous damping control (CDC) that has been developed in recent years. In this work, the ideal yaw angle rate and sideslip angle of the mass center are calculated deriving an ideal monorail model with two degrees of freedom. Then, a direct yaw moment proportional-integral-differential control strategy for ESP is proposed as the foundation of VDM. In addition, a multi-objective fuzzy continuous damping control (MFCDC) is proposed to achieve comfort, handling stability, and rollover prevention. The effect of the MFCDC strategy is analyzed and verified through a sine wave steer input test, double line change test, and fishhook test. The results indicate that MFCDC-ESP has a significant advantage in preventing rollover. MFCDC-ESP can maintain the optimized distribution of damping force through its own compensation under possible instability and predict the critical stable state to some extent. MFCDC-ESP exhibits strong real-time sensitivity to the control state of the damping force of each wheel. Hence, it can ensure the comfort of passengers under good driving conditions and exert strong adaptability and control effects under extreme working conditions.

An energy management control strategy based on the instantaneous optimization method of equivalent consumption minimization strategy (ECMS) under motor power compensation for mild hybrid vehicles is proposed in this study to improve fuel economy and ensure the dynamic performance of cars. A mild hybrid platform is built, and the future supplementary model of electric energy and the future consumption model of electric energy are established according to different power flow directions. It determines the equivalent fuel consumption rate of powertrain as the objective function by defining the equivalent factor and corresponding derivation, carries out optimization calculation, and obtains the energy distribution relationship between the engine and the motor. The motor power compensation strategy based on the control strategy is adopted to solve the effect of turbocharged engines’ transient response on vehicle dynamics and fuel economy. The actual results showed that vehicle power and fuel economy can be improved under the control strategy and compensation strategy design. Meanwhile, different motors allow the compensating coefficient to have different power-boosting and fuel economy effects.

Many consumers and scholars currently focus on driving assistance systems (DAS) and intelligent transportation technologies. The distance and speed measurement technology of the vehicle ahead is an important part of the DAS. Existing vehicle distance and speed estimation algorithms based on monocular cameras still have limitations, such as ignoring the relationship between the underlying features of vehicle speed and distance. A multi-cue fusion monocular velocity and ranging framework is proposed to improve the accuracy of monocular ranging and velocity measurement. We use the attention mechanism to fuse different feature information. The training method is used to jointly train the network through the distance velocity regression loss function and the depth loss as an auxiliary loss function. Finally, experimental validation is performed on the Tusimple dataset and the KITTI dataset. On the Tusimple dataset, the average speed mean square error of the proposed method is less than $0.496 {\mathrm{m}}^2/{\mathrm{s}}^2$, and the average mean square error of the distance is $5.695 {\mathrm{m}}^2$. On the KITTI dataset, the average velocity mean square error of our method is less than $0.40 {\mathrm{m}}^2/{\mathrm{s}}^2$. In addition, we test in different scenarios and confirm the effectiveness of the network.