# find the closest mid-lane point to the agent centroid
    dists_to_mid_points = np.linalg.norm(centroid - lanes_mid_points, axis=1)
    i_min_dist_to_mid = dists_to_mid_points.argmin()
    mid_point = lanes_mid_points[i_min_dist_to_mid]

    # disqualify the point if there is any left-lane or right-lane point closer to mid_point than the centroid
    min_dist_to_left = np.min(np.linalg.norm(mid_point - lanes_left_points, axis=1))
    min_dist_to_right = np.min(np.linalg.norm(mid_point - lanes_right_points, axis=1))

    dist_to_centroid = np.linalg.norm(mid_point - centroid)

    if dist_to_centroid > min_dist_to_left or dist_to_centroid > min_dist_to_right:
        if verbose:
            print(f'Agent {agent_name} discarded, dist_to_centroid: {dist_to_centroid},'
                  f' min_dist_to_left: {min_dist_to_left}, min_dist_to_right: {min_dist_to_right}')
        return False
    if verbose:
        print(f'Agent {agent_name} is OK')
    return True