Check if a new observation (phi, y) is valid according to a confidence ellipsoid on theta. :param y: observation :param phi: feature

Is a position visible in the surface? :param vec: a position :param margin: margins around the frame to test for visibility

Compute the signed distance to another vehicle along a lane. :param v: the other vehicle :param vehicle: the autonomous vehicle :par

MOBIL lane change model: Minimizing Overall Braking Induced by a Lane change The vehicle should change lane only if: - after cha

MOBIL lane change model: Minimizing Overall Braking Induced by a Lane change The vehicle should change lane only if:

Set the origin of the displayed area to center on a given world position. :param position: a world position [m]

Normalize the observation values. For now, assume that the road is straight along the x axis. :param Dataframe df: observati

Normalize the observation values. For now, assume that the road is straight along the x axis. :param Dataframe df: observati

Description for this experiment: + medium: maacktr, regionalR + seed = 0

Description for this experiment: + easy: globalR + seed = 0

Step the boundary parts of the current state interval 1. Split x_i(t) into two upper and lower intervals x_i_-(t) and x_i_+(t)

Check if a point is inside a rectangle :param point: a point (x, y) :param rect_min: x_min, y_min :param rect_max: x_max, y_max

Check if a point is inside a rotated rectangle :param point: a point :param center: rectangle center :param length: rectangle length

Get a matrix polytope from a parametrized matrix function and parameter box :param parametrized_f: parametrized matrix function :param p

Get the absolute position and heading along a route composed of several lanes at some local coordinates. :param route: a planned rou

Predict the future trajectory of the vehicle given a sequence of actions. :param actions: a sequence of future actions. :par

Initialize the LPV models used for interval prediction.

Step the interval predictor dynamics :param dt: timestep [s]

Resolve a conflict between two vehicles by determining who should yield :param v1: first vehicle :param v2: second vehicle

implementation of safety supervisor

Breadth-first search of shortest path from start to goal. :param start: starting node :param goal: goal node :return

Get the observation space.

Control the speed of the vehicle. Using a simple proportional controller. :param target_speed: the desired speed :return: an acceler

Find the index of the closest speed allowed to a given speed. :param speed: an input speed [m/s] :return: the index of the c

Steer the vehicle to follow the center of an given lane. 1. Lateral position is controlled by a proportional controller yielding a lateral s

Steer the vehicle to follow the center of an given lane. 1. Lateral position is controlled by a proportional controller yielding a lateral s

Steer the vehicle to follow the center of an given lane. 1. Lateral position is controlled by a proportional controller yielding a l

Linear controller with respect to parameters. Overrides the non-linear controller ControlledVehicle.steering_control() :par

Propagate the vehicle state given its actions. Integrate a modified bicycle model with a 1st-order response on the steering wheel dy

Step an interval predictor with polytopic uncertainty. :param x_i: state interval at time t :param dt: time step :re

Step an interval predictor with box uncertainty. :param x_i: state interval at time t :param dt: time step :return:

Store the current model, min and max states to a trajectory list.

Draw a striped line on one side of a lane, on a surface. :param lane: the lane :param surface: the pygame surface :p

Ensure that the dynamics matrix A0 is Metzler. If not, design a coordinate transformation and apply it to the model and state interv

Update a list of possible routes that this vehicle could be following. - Add routes at the next intersection - Step the curr

the world position of the center of the displayed window.

Perform a deep copy but without copying the environment viewer.

A Linear Parameter-Varying system: dx = (a0 + sum(da))(x - center) + bd + c :param x0: initial state :param a0: nom

:param env: The environment to observe :param features: Names of features used in the observation :param vehicles_count: Numb

:param env: The environment to observe :param features: Names of features used in the observation :param vehicles_count: Numb

Create a continuous action space. :param env: the environment :param acceleration_range: the range of acceleration values [m

Create a discrete action space of meta-actions. :param env: the environment :param longitudinal: include longitudinal action

:param theta_a_i: The interval of possible acceleration parameters :param theta_b_i: The interval of possible steering parameters

New road. :param network: the road network describing the lanes :param vehicles: the vehicles driving on the road :p

:param road: the road instance where the object is placed in :param position: cartesian position of object in the surface :pa

New straight lane. :param start: the lane starting position [m] :param end: the lane ending position [m] :param widt