自行车模型与汽车模型的混合策略在自动驾驶中的多维度协同优化

    class EnhancedModelSelector(DynamicModelSelector):
    def predict_acceleration(self, trajectory):
        # 使用三次样条插值预测未来3秒加速度
        return spline_interpolation(trajectory)
        
    def select_model(self, state):
        predicted_accel = self.predict_acceleration(state['trajectory'])
        if state['speed'] < 15 and predicted_accel < 0.2g:
            return 'BicycleModel'
        elif state['curvature'] > 0.05:  # 高曲率路径
            return 'DualTrackModel'
        else:
            return self.fallback_model(state)

    class RL_Scheduler(ResourceScheduler):
    def __init__(self):
        self.q_table = np.load('resource_qtable.npy')
        
    def allocate_gpu(self, task):
        state = self.get_gpu_state()
        action = np.argmax(self.q_table[state])
        return self.apply_allocation(action)
    
    def update_q_table(self, reward, new_state):
        # 使用Q-learning更新策略
        self.q_table[self.current_state] += self.lr * (
            reward + self.gamma * np.max(self.q_table[new_state]) 
            - self.q_table[self.current_state])

    def plot_braking_performance():
    speeds = np.linspace(10, 120, 12)
    distances = [calc_braking_dist(v) for v in speeds]
    plt.plot(speeds, distances, marker='o')
    plt.xlabel('Speed (km/h)')
    plt.ylabel('Braking Distance (m)')