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Autonomous Emergency Braking (AEB)#

Purpose / Role#

autonomous_emergency_braking is a module that prevents collisions with obstacles on the predicted path created by a control module or sensor values estimated from the control module.

Assumptions#

This module has following assumptions.

  • It is used when driving at low speeds (about 15 km/h).
  • The predicted path of the ego vehicle can be made from either the path created from sensors or the path created from a control module, or both.
  • The current speed and angular velocity can be obtained from the sensors of the ego vehicle, and it uses point cloud as obstacles.

aeb_range

Limitations#

  • AEB might not be able to react with obstacles that are close to the ground. It depends on the performance of the pre-processing methods applied to the point cloud.
  • Longitudinal acceleration information obtained from sensors is not used due to the high amount of noise.
  • The accuracy of the predicted path created from sensor data depends on the accuracy of sensors attached to the ego vehicle.

Parameters#

Name Unit Type Description Default value
use_predicted_trajectory [-] bool flag to use the predicted path from the control module true
use_imu_path [-] bool flag to use the predicted path generated by sensor data true
voxel_grid_x [m] double down sampling parameters of x-axis for voxel grid filter 0.05
voxel_grid_y [m] double down sampling parameters of y-axis for voxel grid filter 0.05
voxel_grid_z [m] double down sampling parameters of z-axis for voxel grid filter 100000.0
min_generated_path_length [m] double minimum distance for a predicted path generated by sensors 0.5
expand_width [m] double expansion width of the ego vehicle for the collision check 0.1
longitudinal_offset [m] double longitudinal offset distance for collision check 2.0
t_response [s] double response time for the ego to detect the front vehicle starting deceleration 1.0
a_ego_min [m/ss] double maximum deceleration value of the ego vehicle -3.0
a_obj_min [m/ss] double maximum deceleration value of objects -3.0
prediction_time_horizon [s] double time horizon of the predicted path generated by sensors 1.5
prediction_time_interval [s] double time interval of the predicted path generated by sensors 0.1
aeb_hz [-] double 　frequency at which AEB operates per second 10

Inner-workings / Algorithms#

AEB has the following steps before it outputs the emergency stop signal.

  1. Activate AEB if necessary.

  2. Generate a predicted path of the ego vehicle.

  3. Get target obstacles from the input point cloud.

  4. Collision check with target obstacles.

  5. Send emergency stop signals to /diagnostics.

We give more details of each section below.

1. Activate AEB if necessary#

We do not activate AEB module if it satisfies the following conditions.

  • Ego vehicle is not in autonomous driving state
  • When the ego vehicle is not moving (Current Velocity is very low)

2. Generate a predicted path of the ego vehicle#

AEB generates a predicted path based on current velocity and current angular velocity obtained from attached sensors. Note that if use_imu_path is false, it skips this step. This predicted path is generated as:

\[ x_{k+1} = x_k + v cos(\theta_k) dt \\ y_{k+1} = y_k + v sin(\theta_k) dt \\ \theta_{k+1} = \theta_k + \omega dt \]

where \(v\) and \(\omega\) are current longitudinal velocity and angular velocity respectively. \(dt\) is time interval that users can define in advance.

3. Get target obstacles from the input point cloud#

After generating the ego predicted path, we select target obstacles from the input point cloud. This obstacle filtering has two major steps, which are rough filtering and rigorous filtering.

Rough filtering#

In rough filtering step, we select target obstacle with simple filter. Create a search area up to a certain distance (default 5[m]) away from the predicted path of the ego vehicle and ignore the point cloud (obstacles) that are not within it. The image of the rough filtering is illustrated below.

rough_filtering

Rigorous filtering#

After rough filtering, it performs a geometric collision check to determine whether the filtered obstacles actually have possibility to collide with the ego vehicle. In this check, the ego vehicle is represented as a rectangle, and the point cloud obstacles are represented as points.

rigorous_filtering

4. Collision check with target obstacles#

In the fourth step, it checks the collision with filtered obstacles using RSS distance. RSS is formulated as:

\[ d = v_{ego}*t_{response} + v_{ego}^2/(2*a_{min}) - v_{obj}^2/(2*a_{obj_{min}}) + offset \]

where \(v_{ego}\) and \(v_{obj}\) is current ego and obstacle velocity, \(a_{min}\) and \(a_{obj_{min}}\) is ego and object minimum acceleration (maximum deceleration), \(t_{response}\) is response time of the ego vehicle to start deceleration. Therefore the distance from the ego vehicle to the obstacle is smaller than this RSS distance \(d\), the ego vehicle send emergency stop signals. This is illustrated in the following picture.

rss_check

5. Send emergency stop signals to /diagnostics#

If AEB detects collision with point cloud obstacles in the previous step, it sends emergency signal to /diagnostics in this step. Note that in order to enable emergency stop, it has to send ERROR level emergency. Moreover, AEB user should modify the setting file to keep the emergency level, otherwise Autoware does not hold the emergency state.