A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles, this study proposes a continuous multiple scanning recursive matching algorithm based on a single-line LIDAR sensor. Radar recursive scanning is used to obtain the multiple superposition data of echo signals, and coordinate matching is realized between historical scanning data and current scanning data. Simultaneously, the sensor height deviation and pitch angle deviation of the sensors are regressed to obtain an accurate pavement elevation. Considering the control effect of the active vehicle suspension, a vehicle suspension model with seven degrees of freedom is established. The semi-active suspension controller is constructed using a diagonal recursive neural network algorithm, and the neural network weight is trained using a genetic algorithm. In addition, a preview diagonal recursive neural network control strategy for semi-active suspension, based on the combination of road elevation information, is proposed. The results of a hardware-in-the-loop co-simulation, which was conducted based on the Simulink control model and dSPACE real-time simulation, revealed that the ride comfort and stability of the vehicle were improved owing to a preview of the elevation information of the road ahead and the active adjustment of the shock absorber of the suspension system.

Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles.

Lane changing assistance in autonomous vehicles is a popular research topic. Scene modeling of the driving area is a prerequisite for lane changing decisio

Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL’s policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments. On the other hand, the complexity of these environments leads to low sample efficiency in RL, making it difficult to efficiently learn driving policies. To address these challenges, we propose an innovative Knowledge Distillation-Enhanced Behavior Transformer (KD-BeT) framework. Building on the successful application of Transformers in large language models, we introduce the Behavior Transformer as the policy network in RL, using observation–action history as input for sequential decision-making, thereby leveraging the Transformer’s contextual reasoning capabilities. Using a teacher–student paradigm, we first train a small-capacity teacher model quickly and accurately through IL, then apply knowledge distillation to accelerate RL’s training efficiency and performance. Simulation results demonstrate that KD-BeT maintains fast convergence and high asymptotic performance during training. In the CARLA NoCrash benchmark tests, KD-BeT outperforms other state-of-the-art methods in terms of traffic efficiency and driving safety, offering a novel solution for addressing real-world autonomous driving tasks.

Skilled drivers have the driving behavioral characteristic of pre-sighted following, and similarly intelligent vehicles need accurate prediction of future trajectories. The LSTM (Long Short-Term Memory) is a common model of trajectory prediction. The existing LSTM models pay less attention to the interactions between the target and the surrounding vehicles. Furthermore, the impacts on future trajectories of the target vehicle have also barely been a focus of the current models. On these bases, a Residual Attention-based Long Short-Term Memory (RA-LSTM) model was proposed, an interaction tensor based on the surroundings of the target vehicle at the predictive moments was constructed and the weight coefficients of the interaction tensor for the surrounding vehicles relative to the target vehicle were calculated and re-programmed in this study. The proposed RA-LSTM model can implicitly represent the different degrees of influence of the surrounding vehicles on the target vehicle; the probability distributions of the future trajectory coordinates of the target vehicle is predicted based on the extracted interaction features. The RA-LSTM model was tested and verified in multiple scenarios by using the NGSIM (next generation simulation) public dataset, and the results showed that the prediction accuracy of the proposed model is significantly improved compared with the current LSTM models.

Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there are still shortcomings in personalized prediction (particularly in accounting for individual differences in driving experience, cognitive abilities, and physiological responses). To gain a comprehensive understanding of the characteristics and applicability of take-over time prediction methods, this review covers four aspects: literature search information, factors influencing take-over time, data acquisition and processing methods, and take-over time prediction methods. Through literature search, research hotspots in recent years have been summarized, revealing the main research directions and trends. Key factors influencing take-over time, including driver factors, autonomous driving systems, and driving environments, are discussed. Data preprocessing stages, including data acquisition and processing, are systematically analyzed. The advantages and disadvantages of classical statistical, machine learning, and cognitive architecture models are summarized, and the shortcomings in current research are highlighted (for instance, the limited generalizability of models trained predominantly on simulator data to real-world driving scenarios). By thoroughly summarizing the strengths and weaknesses of existing research, this review explores under-researched areas and future trends, aiming to provide a solid theoretical foundation and innovative research perspectives for optimizing take-over time prediction, thereby promoting the widespread application and efficient development of autonomous driving technology.