human
1.3 Acceleration Command
Next, we implement the acceleration equation specified by IDM: 
$$\dot{v} = a \left[ 1- \left( \frac{v}{v_0} \right)^\delta -\left( \frac{s^}{h} \right)^2 \right] \textbf{, with } \ s^ := s_0 + \max \left( 0, vT + \frac{v\Delta v}{2\sqrt{ab}} \right)$$
The vehicle's velocity v is fetched by getter method get_speed of the environment's vehicles object, as is the id of the lead vehicle lead_id and headway h. 
Input-checking to ensure that overly small headways are not used is performed, as well as a step to set $s^$ properly when no car is ahead of the vehicle being controlled. If there is a lead vehicle, $s^$ is calculated as described, and then the IDM acceleration is returned.

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assistant
#Here is the Python code you requested.
class IDMController(BaseController):
    def __init__(self, veh_id, v0=30, T=1, a=1, b=1.5, 
                 delta=4, s0=2, s1=0, time_delay=0.0, 
                 dt=0.1, noise=0, fail_safe=None, car_following_params=None):
        """Docstring eliminated here for brevity"""
        BaseController.__init__(self, veh_id, car_following_params,
                                delay=time_delay, fail_safe=fail_safe,
                                noise=noise)
        self.v0 = v0
        self.T = T
        self.a = a
        self.b = b
        self.delta = delta
        self.s0 = s0
        self.s1 = s1
        self.dt = dt

        
    ##### Below this is new code #####
    def get_accel(self, env):
        v = env.k.vehicle.get_speed(self.veh_id)
        lead_id = env.k.vehicle.get_leader(self.veh_id)
        h = env.k.vehicle.get_headway(self.veh_id)

        # negative headways may be registered by sumo at intersections/
        # junctions. Setting them to 0 causes vehicles to not move; therefore,
        # we maintain these negative headways to let sumo control the dynamics
        # as it sees fit at these points.
        if abs(h) < 1e-3:
            h = 1e-3

        if lead_id is None or lead_id == '':  # no car ahead
            s_star = 0
        else:
            lead_vel = env.k.vehicle.get_speed(lead_id)
            s_star = self.s0 + max(
                0,
                v * self.T + v*(v-lead_vel) / (2*np.sqrt(self.a*self.b)))

        return self.a * (1 - (v/self.v0)**self.delta - (s_star/h)**2)