An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems: A highway merging case study

Abstract

Navigating automated driving systems (ADSs) through complex driving environments is difficult. Predicting the driving behavior of surrounding human-driven vehicles (HDVs) is a critical component of an ADS. This paper proposes an enhanced motion-planning approach for an ADS in a highway-merging scenario. This method utilizes the results of two aspects: the driving behavior and long-term trajectory of surrounding HDVs, which are coupled using a hierarchical model that is used for the motion planning of an ADS to improve driving safety. An unsupervised clustering algorithm is utilized to classify HDV drivers into two categories: aggressive and normal, as part of predicting their driving behaviors. Subsequently, a logistic regression model is employed for driving style prediction. For trajectory prediction, a transformer-based model that concentrated parallelization computations on longer sequence predictions via a self-attention mechanism is developed. Based on the predicted driving styles and trajectories of the surrounding HDVs, an intelligent decision-making strategy is utilized for ADS motion planning. Finally, real-world traffic data collected from drones on a highway ramp in Xi’an is used as a case study to train and evaluate the proposed model. The results demonstrate that the proposed approach can predict HDVs merging trajectories with a mean squared error (MSE) smaller than 0.125 at 30 s away from the merging point, outperforming existing approaches in terms of predictable duration and accuracy. Furthermore, it exhibited safety improvements by adjusting the ADS motion state in advance with good predictive power for the surrounding HDVs’ motion. For both normal and aggressive driving styles, the ADS trajectories reached optimal safety at a prediction horizon of at least 10 s and over 6.67 s, respectively, when evaluating both time-to-collision (TTC) and deceleration rate to avoid a crash (DRAC) metrics. This indicates that our proposed procedures have improved safety for AVs with long-term predictive capabilities (i.e., over 6 s) for surrounding HDVs. Interestingly, the safety performance does not continue to improve with the increase in prediction horizon once the optimal safety performance (highest TTC and lowest DRAC) has been achieved for both normal and aggressive styles. This suggests that an excessively long prediction horizon (e.g., 30 s) is not always beneficial for safety performance of ADS in our case study. When ADS encounter aggressive HDVs, having a predicted surrounding HDV trajectory can extremely reduce crash risk and maintain a safe situation. This demonstrates that planning ADS trajectories with HDV predictions has significant effects on safety, especially for aggressive driving styles as compared to normal driving styles.