Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

Steering feel holds a main role in driving, making steering feedback torque (SFT) generation an important research of steer-by-wire (SBW) system. Authentic steering feel provides sensations comparable to conventional steering systems, while the SFT model aligns with driver preferences, reducing cognitive load and fatigue, thereby improving vehicle safety. Current research on driver-preferred SFT is limited to real-world trials, a method often time-intensive and suboptimal. This paper presents an innovative approach for obtaining optimal SFT with reinforcement learning (RL) guided by human reward model. The human reward model, trained on human feedback data, is introduced to guide SFT model’s optimization. Given complexity of assessing human evaluations, the reward model is validated with virtual data. Building upon previous data-driven SFT modeling, a fine-tuning method leveraging the RL method is presented, using human reward model’s outputs. This approach retains the nonlinear predictive capabilities of the pre-trained model while aligning with human evaluations. Results confirm the human reward model’s ability to replicate human SFT standards. Models adapted using this network retain the nonlinear predictive capabilities of the initial network, even under high-frequency inputs. Overall, this work pioneers the integration of human feedback into SFT optimization, providing novel insights for the advancement of SBW systems.

Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models.

Autonomous driving represents the future of transportation, and simulation testing is a critical technology for ensuring the safety and reliability of autonomous driving. In simulation systems, a real-time and accurate radar model is crucial for enhancing the confidence of autonomous driving simulation systems. Existing radar models are typically simplistic and cannot accurately reflect the detection results of real radars. This paper introduces a data-driven radar modeling method that analyzes radar detection mechanisms to identify the model’s input parameters. Furthermore, this method decouples the model’s output parameters based on the differing mechanisms of radar output parameters and tailors model structures to align with the mechanistic characteristics of each parameter. Subsequently, considering the varied influences among input parameters, a segmented model structure is proposed that reduces input dimensionality while retaining critical information. Lastly, the model outputs are optimized using Kalman filtering. This study collects radar data from real vehicles to train the radar model. Testing has demonstrated that the radar model developed by this paper’s method yields outputs very close to actual radar predictions, with a significant improvement in prediction accuracy compared to traditional radar models. The model operates in approximately 0.8 ms, markedly faster than the real radar’s 72-millisecond detection cycle. This demonstrates that the radar model developed using this modeling method can predict radar detection results accurately and in real-time.