Accurately predicting the future trajectory of surrounding vehicles is critical for autonomous driving. However, this task still faces challenges in reducing computational costs, modeling complex interactions, and enhancing prediction realism. To this end, we proposed a multimodal trajectory prediction model for autonomous vehicles based on VectorNet-Interaction-GAN, named VIG. Specifically, VIG takes the historical trajectories and high-definition map information as inputs. Then, the improved VectorNet is employed as a feature extraction module to encode the vectorized inputs, effectively avoiding the high rendering loss and computational density associated with rasterized methods. Next, to capture complex interactions, we introduce an interaction module comprising four sub-networks (Vehicles to Lanes, Lanes to Lanes, Lanes to Vehicles and Vehicles to Vehicles), which utilize the attention mechanism and the polyline subgraph to model the different entities. Finally, a GAN-based framework is incorporated to enhance the realism and plausibility of the predicted trajectories. Extensive experiments on the Argoverse motion dataset demonstrate that VIG outperforms the existing methods while maintaining low model complexity.

To study the influence of injection time and injection volume on the working process of a two-stroke kerosene direct injection engine, an experimental study was carried out on an improved two-stroke inline three-cylinder gasoline engine, combined with calculations and analysis with GT-POWER and AVL FIRE software. The results showed that when the injection end angle increased from 50° to 70° before the top dead center (BTDC), the average pressure and temperature in the cylinder increased rapidly, the peak value of pressure and temperature and the cumulative heat release increased, and the combustion process in the cylinder was more sufficient. The fuel injection volume was set to 7.5 mg, 8 mg, and 8.5 mg. With increasing fuel injection volume, the average pressure and average temperature first increased and then decreased, the peak value gradually increased, the heat release rate and cumulative heat release increased sharply, the corresponding time gradually advanced, and the peak value gradually increased. With increasing fuel injection volume, CO, NO, and soot gradually increased, while CO₂ slightly decreased.

To study the influence of control parameters under cold-start and low-load conditions on the performance of a heavy-oil, two-stroke, direct-injection, ignition engine for use in aviation, the operation of a two-stroke, direct-injection engine was studied in a bench test. The results were as follows: ① When the ambient temperature is 15 °C, the battery voltage is 12.4 V, and the peak speed of the starting motor is 1200 r/min. As the concentration factor increases, the cold-start speed increases, and the fuel consumption increases. The influence on the cold start is reduced after reaching a certain concentration. The cold-start time decreases with the increasing magnetization pulse width. The cold-start time is the shortest at an oil–gas interval of 6 ms. ② Under small-load conditions of 3000 r/min and 14% to 16% throttle, a higher ignition energy increases the engine power. Pollutant emissions are the lowest when the fuel injection is 7.5 mg and the excess air coefficient is approximately 1.1.

With the rapid development of intelligent sensing and control technologies, map building and path planning for indoor mobile robots have been widely applied in indoor navigation scenarios. However, existing SLAM methods suffer from low loop closure detection accuracy and map drift, while existing global path planning methods often yield non-smooth paths with suboptimal search efficiency. To this end, we propose an integrated SLAM and path planning method leveraging an improved Gmapping algorithm and A* algorithm. Firstly, a SLAM method integrating the Gmapping-Cartographer algorithm is introduced, which incorporates an improved Gmapping algorithm into Cartographer’s loop closure detection module. This approach ensures mapping accuracy while effectively reducing mapping time and mitigating localization errors and map drift. Secondly, a path planning method based on the improved A* algorithm is introduced. The heuristic function of the A* algorithm is refined by adjusting the weights of the actual cost and the estimated cost, which accelerates search convergence and improves planning accuracy. Moreover, to alleviate the prevalence of excessive path inflection points and large turning angles, the Bézier curve method is introduced for smoothing, ensuring path fluidity and fewer turns. Finally, the effectiveness of the proposed method is validated in a Gazebo virtual simulation environment and a real laboratory environment. Experimental results demonstrate that our SLAM method demonstrates superior performance in mapping accuracy and successfully constructs high-precision maps. Simultaneously, our path planning method shows significant performance advantages over the original A*, ant colony optimization, and genetic algorithm in maps of different scales and achieves trajectories with fewer inflection points and greater smoothness.

In recent years, the smelting industry has been continuously developing, and temperature is the most basic parameter in the smelting process. It can help furnace workers understand the state of the metal and carry out corresponding smelting operations based on the smelting process, thereby ensuring the quality of the metal. In most smelting factories, temperature measurement still relies on the use of ordinary thermocouples. Ordinary thermocouples have the disadvantages of short lifespan, high cost, and inability to continuously measure temperature. Medium frequency electric furnaces are widely used in the smelting industry due to their advantages of high efficiency, energy conservation, and stable operation. This article utilizes the characteristic that the resistivity of the furnace material increases with temperature to build a collection circuit. By collecting the reflection resistance and temperature of the furnace material related to resistivity, and based on the traditional Transformer model and the long short-term memory (LSTM) model, a Transformer-LSTM hybrid model is proposed to predict and analyze the temperature of the medium frequency furnace material. The results show that the Transformer—LSTM hybrid model, compared to the traditional Transformer model and LSTM model, has better performance in predicting the overall error and fluctuation of data and can be applied to temperature prediction.