The decision-making system of intelligent vehicles is the core component of an advanced driving system for both passenger vehicles and commercial vehicles. Finding ways to improve decision-making strategies to suit the complex and unfamiliar environments is a standing problem for traditional rule-based methods. This paper proposes a semi-rule-based decision-making strategy for heavy intelligent vehicles based on the Deep Deterministic Policy Gradient algorithm. Firstly, according to the car-following characteristics, the problems of high dimensions and a large amount of data in vehicle action space and state space are solved by dimension reduction and interval reduction to accelerate the training process. Subsequently, an accurate three-axle vehicle load model is established to calculate the load transfer rate value and carry out active control to increase the roll stability of heavy vehicles at high-speed corners. Furthermore, the Deep Deterministic Policy Gradient algorithm is developed based on the reward function and update function to achieve adaptive cruise control objectives for heavy vehicles on different curvature roads. Finally, the effectiveness and robustness of the algorithm are verified through simulation experiments.

Traditional adaptive cruise control (ACC) would choose to follow when encountering low-speed cars in front, but this may lead to low driving efficiency. This paper proposes a controller of enhanced adaptive cruise control with lane-change assistance (LCACC) for an articulated vehicle. A two-layer hierarchical control structure is adopted in this study. The upper determines high-level commands, while the bottom consisting of two modified deep deterministic policy gradient (DDPG) networks, controls the steering wheel and throttle/brake, respectively, according to the commands made by the upper layer. The vehicle’s lateral and longitudinal control are decoupled and controlled by two modified DDPG networks. Through proper design of the state and reward function, the actions of the articulated vehicle such as steering and acceleration/braking are closer to those of humans, thus ensuring ride comfort. Compared with traditional ACC, the LCACC proposed in this study increases the driving efficiency of the articulated vehicle by an average of 16% when there is a low-speed lead car.

This paper presents a gain scheduling output feedback control method to reduce driver workload and improve driving performance by considering input saturation. The driver–vehicle system model is developed by considering tire cornering stiffness uncertainties and different driver parameter uncertainties. Meanwhile, the input saturation is also considered in the driver-vehicle system. A quadratic Lyapunov function is designed to solve the optimization problem with uncertainties and input saturation. The results, which are based on the MATLAB-CarSim co-simulation platform, indicate that the robust controller not only improves the convergence rate of the state but also reduces the steering workload of the driver.

Against the backdrop of the booming development of autonomous driving technology, to address the problems of insufficient accuracy in long-term prediction and incomplete interaction modeling for pedestrian trajectory prediction from a first-person perspective, this paper proposes the Fusion-Hierarchical Graph Trajectory Network (FHGT-Net). This network first utilizes a Multi-Layer Perceptron (MLP) to generate prior future trajectories, which are then concatenated with historical trajectories to form fused features. Subsequently, an encoder-decoder structure is utilized. The encoder, composed of local sub-graphs and a global graph, extracts features from the fused data. The local sub-graphs process individual pedestrian trajectories through multiple MLP layers, layer normalization, and Rectified Linear Unit (ReLU) activation, while the global graph, leveraging a multi-head attention mechanism, models interactions among pedestrians. Finally, the decoder, based on a Long Short-Term Memory (LSTM) network, decodes the encoder output to iteratively derive the most likely future trajectories for each pedestrian. Experimental results show that, in the PIE (Pedestrian In-car Environment) dataset, compared with the current advanced level, this model reduces the Average Displacement Error (ADE) by 9.9% and the Final Displacement Error (FDE) by 7.5%, significantly improving the accuracy. In addition, this model also performs well in real-vehicle experiments. The research results provide a reliable solution for pedestrian trajectory prediction from a first-person perspective, especially for predictions in multi-pedestrian interaction scenarios, which is of great significance. Plain Language Summaries Understanding how people move on the road is critical for self-driving cars to make safe decisions. This study introduces a new artificial intelligence model called FHGT-Net, designed to predict the walking paths of pedestrians as seen from a car's front camera. Traditional models often struggle to make accurate long-term predictions and to correctly understand how multiple pedestrians influence each other's movements. FHGT-Net solves these problems through a new fusion-hierarchical design. It first combines the car's observations of past pedestrian movements with early guesses of where they might go next. Then, it uses a graph network to model both individual movement patterns and group interactions among pedestrians. Finally, a recurrent neural network predicts where each pedestrian is most likely to move in the next few seconds. When tested on a public dataset called PIE (Pedestrian In-car Environment), FHGT-Net outperformed existing methods, reducing prediction errors by about 10%. It also showed strong performance in real driving tests. In simple terms, this model helps self-driving systems “read” pedestrian behavior more accurately and respond safely in busy environments. The findings bring autonomous driving one step closer to understanding human motion in real-world traffic.