An automated vehicle follows another vehicle (co) on a straight road. Suddenly, the co carries out an emergency braking maneuver.
In this scenario, both vehicles are in the same lane of a straight road. It is the task of the following vehicle (ego) to avoid a collision with the vehicle in front (co) using Adaptive Cruise Control (ACC) as well as Automated Emergency Braking (AEB) functions. Initially, the vehicle in front moves with a velocity v_co, the following vehicle moves with a speed v_ego, and there is a time gap of d_t between the vehicles. The vehicle in front immediately starts to brake with a deceleration of a_co until it reaches a speed of v_co_min_frac * v_co.
The scenario is illustrated below:
| Input | Unit | Min | Max | Type | Explanation |
|---|---|---|---|---|---|
| a_co | m/s^2 | -1 | -10 | continuous | deceleration of the co |
| v_co_min_frac | 0.1 | 1.0 | continuous | final velocity of the co as a fraction of v_co | |
| d_t | s | 0.5 | 3.0 | continuous | initial time gap between the co and ego |
| v_co | km/h | 80 | 150 | continuous | initial velocity of the co |
| v_ego | km/h | 80 | 150 | continuous | initial velocity of the ego |
| Output | Unit | Type | Explanation |
|---|---|---|---|
| TTC_min | s | continuous | minimal time to collision (TTC) in longitudinal direction |
| d_min | m | continuous | minimal distance in longitudinal direction |
| collision | binary | collision indicator based on rough bounding box |
Both datasets contain concrete scenarios which are evenly distributed within the input space defined over the inputs. The train_validation dataset is generated based on the Sobol sequence, the test dataset is generated based on pseudo-random numbers generated by numpy.