Chenhui Yin

Roundabouts require autonomous vehicles to adapt their speed to roadway geometry and nearby traffic. This study presents a longitudinal speed-planning method based on a modified Social Force Model (SFM) that reproduces speed-profile characteristics observed in human-driven roundabout trajectories. The driving-force component combines a geometry-conditioned desired-speed profile with a bounded harmonic term, and the repulsive-force component accounts for direction-dependent interaction with a nearby vehicle. The model was calibrated by nonlinear least squares using 336 retained trajectories from the rounD naturalistic driving dataset. Relative to calibrated constant-speed baselines, the in-sample Root Mean Square Error (RMSE) decreased by up to 65.5% (to 0.698 m/s) for straight maneuvers. The corresponding reduction for left-turn maneuvers was 64.0% (to 0.413 m/s). These results show improved agreement with observed longitudinal speed profiles at the studied site, while broader behavioral validity and cross-site generalization remain to be established.

Planning paths for Frenet-based autonomous vehicles (AVs) at roundabouts is difficult without complete and smooth reference paths. In such situations, the interpolating curve planner is often used to create segmented reference paths from simplified geometric roundabout data. While this method ensures curvature continuity within each curve segment, the continuity at the junctions of these segments is poor. Additionally, the determination of merging and diverging point positions at roundabouts has not been thoroughly explored. This paper introduces a novel approach using 5th-order Bézier curves to plan piecewise reference paths for AVs at roundabouts. The proposed method enhances endpoint curvature continuity of the Bézier curves and improves adaptability to non-standard roundabouts. A well-designed objective function is created to optimize both the geometric continuity parameters of the Bézier curves and the positions of merging and diverging points in the circulatory roadway. This function takes into account key factors, including path length and smoothness. Case studies validate the feasibility of maintaining curvature continuity at the endpoints and the method’s ability to generalize across various scenarios, proving its effectiveness for different roundabout structures. The results also confirm the method’s efficacy in generating paths from original geometric roundabout data. Lastly, the acceptable transverse deviations between real-world trajectories and reference paths demonstrate the rationality and practical applicability of this method.

Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.

This paper proposes a dynamic path planning method based on discrete optimization applied to suburban highways or expressways. We optimize the generated candidate points according to the cost function and then generates a local path through the quintic spline curve. The kinematics and obstacle boundary conditions are set up to improve the reliability of the planned path. Meanwhile, the two-dimensional normal distribution obstacle cost function, comfort cost function and acceleration cost function are designed to evaluate candidate points and speed. Various types of roads scenes, including dynamic and static obstacles, such as straight roads, S- bend, are established to verify the method’s feasibility. The simulation shows that the method can efficiently avoid various obstacles and plan an ideal path that complies with traffic laws.