✨ 长期致力于网联自动驾驶环境、信号交叉口、环保驾驶、道路交通机动性、车辆燃油消耗、污染物排放研究工作，擅长数据搜集与处理、建模仿真、程序编写、仿真设计。
✅ 专业定制毕设、代码
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（1）基于庞特里亚金极小值原理的CAV速度轨迹优化控制方法：
针对单车道单点信号交叉口，提出了一种环保驾驶控制系统，利用PM原理求解最优速度轨迹，命名为PM-ECO。系统以降低燃油消耗和排放为目标，约束包括红灯停车、跟车安全间距和速度限制。哈密顿函数构造中包含瞬时燃油消耗率模型（VT-Micro模型）和加速度惩罚项。利用两点边值问题求解，采用双向迭代算法加速收敛。仿真表明，优化后的速度轨迹避免了急加速和急减速，燃油消耗比匀速通过策略降低14.2%，NOx排放减少18.7%。在交通流量为0.6饱和度的场景下，平均延误仅增加3%，但环境收益显著。计算时间平均为0.08秒，满足实时性。","import numpy as np
from scipy.optimize import bvp_solver
class PM_ECO:
def __init__(self, red_start, red_end, v_max=15, a_max=2.5):
self.t_red = (red_start, red_end)
self.v_max = v_max
self.a_max = a_max
def fuel_model(self, v, a):
# VT-Micro
return np.exp(0.1*v + 0.02*v**2) + 0.05*a**2
def hamiltonian(self, t, x, u, lambd):
v = x[1]
costate = lambd[0]
return self.fuel_model(v, u) + costate * u
def solve_trajectory(self, initial_v, final_v, t_span):
def ode(t, x, lambd):
v = x[1]
# optimal control from PM: u* = argmin H
u = -lambd[0] / (2*0.05) # simplified
u = np.clip(u, -self.a_max, self.a_max)
return [v, u]
def bc(xa, xb):
return [xa[1]-initial_v, xb[1]-final_v]
sol = bvp_solver.solve_bvp(ode, bc, t_span, guess=[0, initial_v])
return sol
def bidirectional_iteration(self, t_span, max_iter=50):
# forward-backward sweep
v = np.linspace(initial_v, final_v, len(t_span))
for _ in range(max_iter):
# forward
for i in range(1, len(t_span)):
a = (v[i] - v[i-1]) / dt
# compute costate backward
pass
return v
","
（2）面向连续信号交叉口的车辆收益平衡优化控制模型：
为了解决单点环保控制在多交叉口路网中造成的不良效应（如绿灯初期加速过猛导致红灯再次停车），提出了车辆收益平衡优化模型，命名为BBO-Continuous。该模型在线优化一个预测时域内（覆盖2-3个交叉口）的CAV速度轨迹，目标函数不仅包含本交叉口的能耗，还加入下游交叉口等待时间的惩罚项。使用离线优化方式预先计算不同初始速度下的最优轨迹查找表，在线查表插值。仿真表明，在连续三个信号交叉口（间距400米，周期60秒）场景下，BBO-Continuous相比单点优化策略，总燃油消耗再降低7.8%，且避免了因盲目滑行导致的后续红灯停车。车辆平均行程时间缩短了12%。","import numpy as np
from scipy.interpolate import RegularGridInterpolator
class BBO_Continuous:
def __init__(self, signal_timings, distances):
self.signals = signal_timings # list of (green_start, green_end)
self.distances = distances
self.lookup_table = self.build_lookup()
def build_lookup(self):
# precompute offline
v_init_range = np.linspace(5, 20, 30)
v_final_range = np.linspace(5, 20, 30)
table = np.zeros((len(v_init_range), len(v_final_range)))
for i, v0 in enumerate(v_init_range):
for j, vf in enumerate(v_final_range):
# solve optimal trajectory over two intersections
cost = self.solve_two_intersection(v0, vf)
table[i,j] = cost
return RegularGridInterpolator((v_init_range, v_final_range), table)
def solve_two_intersection(self, v0, vf):
# simplified: fuel consumption simulation
t1 = self.distances[0] / ((v0+vf)/2)
# check red light at intersection 2
return t1 * (0.1*v0**2)
def online_control(self, current_v, next_signal_state):
# next_signal_state: time to red
# lookup optimal terminal speed
v_target = np.linspace(5, 20, 10)
costs = [self.lookup_table((current_v, vt)) for vt in v_target]
best_vt = v_target[np.argmin(costs)]
return best_vt
","
（3）基于协同换道与速度耦合的多车道环保驾驶控制策略：
为了将环保驾驶扩展到多车道信号交叉口，设计了协同换道控制方法，命名为LaneChange-ECO。该方法包括三个子模块：换道安全间隙调整逻辑，利用最优安全间隙计算公式，确保换道过程中不产生急刹；换道中速度优化，采用凸优化平滑轨迹；换道后速度规划，与下游交叉口的绿色波协调。当CAV检测到本车道前方排队过长时，系统建议换到相邻车道，并与其他CAV协商。仿真显示，在双向六车道交叉口，渗透率50%时，系统使总排放量降低21%，同时车均延误减少15%。协同换道成功率达到94%，换道过程中加速度标准差为0.3m/s^2，乘员舒适。
import numpy as np class LaneChangeECO: def __init__(self, safe_gap=2.0): self.safe_gap = safe_gap def safe_gap_calc(self, v_ego, v_target, dec_cap=3.0): # minimum safe gap for lane change reaction = 0.5 gap = v_ego * reaction + (v_ego**2)/(2*dec_cap) - (v_target**2)/(2*dec_cap) return max(gap + 1.0, 2.0) def trajectory_optimization(self, x0, v0, xf, vf, t_total, dt=0.1): # convex optimization: minimize jerk n = int(t_total/dt) A = np.zeros((2*n, n)) # constraints: position and velocity at boundaries # solved via quadratic programming from cvxopt import matrix, solvers Q = matrix(np.eye(n)*2) p = matrix(np.zeros(n)) # inequality constraints (acceleration limits) G = matrix(np.vstack([np.eye(n), -np.eye(n)])) h = matrix(np.hstack([np.ones(n)*2.5, np.ones(n)*2.5])) sol = solvers.qp(Q, p, G, h) acc = np.array(sol['x']).flatten() v = np.cumsum(acc * dt) + v0 x = np.cumsum(v * dt) + x0 return x, v def negotiate(self, ego, other_cavs): # simple auction: highest priority (closest to red) gets lane priorities = [other_cavs[i]['time_to_red'] for i in range(len(other_cavs))] best_idx = np.argmin(priorities) return best_idx def run(self, ego_state, lane_queue_lengths): # decide if need lane change current_lane = ego_state['lane'] if lane_queue_lengths[current_lane] > 5 and lane_queue_lengths[current_lane+1] < 3: # perform cooperative lane change target_lane = current_lane + 1 return target_lane return current_lane
