Closed-Loop Behavior Model

The closed-loop behavior model explicitly considers information feedback to opera- tors, as shown in Figure 2.3. Shia et al. apply the closed-loop behavior model to a semi-autonomous driving application [248, 238, 230, 263, 84].

Problem description. Figure 2.4 illustrates the workflow of semi-autonomous driving. The driver is given a specific driving task, e.g., make turns or go straight. The driver’s actions are influenced by both the driver’s state, e.g., distracted or attentive, and the environmental conditions, e.g., the presence of obstacles. The semi-autonomous controller has three key components. The first component predicts future vehicle trajectories given the driver’s inputs, e.g., steering, acceleration, and braking, under different environmental conditions and driver states. The second component compares the predicted vehicle trajectories with unsafe regions, e.g., an obstacle or road curbs, and decides whether the controller needs to apply a correction to the driver’s inputs. If the controller decides to intervene, the third component computes the control inputs, e.g., steering angle.

The model-based analysis framework must solve three technical problems that correspond to the three components of the semi-autonomous controller shown in Figure 2.4:

Driver  State

Environmental   Condition Driver  Action

Driving  Task Trajectory  _Prediction Intervention_Decision Driver  Input  _Correction Semi-­‐Autonomous   Control

Figure 2.4: The workflow of semi-autonomous driving.

1. At each time point, given the measurements of the driver’s pose in a past time window and the information of the vehicle as well as its environment in a future time window, predict the driver’s action and the resulting vehicle trajectory within a near-future time window.

2. Given the vehicle trajectory predictions and the environmental conditions, de- cide if the vehicle could enter unsafe regions.

3. Given the driver’s input, the vehicle information, and the environmental con- dition, compute correctional control inputs, if necessary, such that the vehicle stays in the safe region.

Methodology overview. Twenty-four drivers participate in a two-hour driving session on the industry driving simulator CarSim. The first hour is for driver data collection, during which the semi-autonomous controller is turned off, and the sec- ond hour is for testing, during which the controller is activated. The drivers need to perform specific driving tasks, e.g., maintain a certain speed and/or keep a safe distance from the leading car. Drivers may be distracted by text messages. The

simulator emulates obstacles by sudden speed drops of the leading car or appear- ances of simulated animals, forcing the driver to take defensive maneuvers. The full combinations of environmental conditions (with or without obstacles) and the driver’s states (with or without phone distraction) yield four possible driving scenar- ios. A Microsoft Kinect monitors the driver pose in real time, and it tracks the 3-D movements of the driver’s joints using computer vision technologies. The simulator records vehicle dynamics measurements, the driver’s inputs, and road information.

Shia et al. propose a procedure that uses the training data to learn the mapping from the driver pose and environmental conditions to the driver’s actions. They associate the driver pose data with the environmental condition at each time step, and apply k-means algorithm [107] to cluster the combined dataset. For each cluster,

they identify the driver’s actions in the next 1.2 seconds time window and pass the

inputs into a vehicle model [231] to predict trajectories.

The expected vehicle trajectories are intersected with unsafe regions that are defined by obstacle locations, the lane boundaries, and the road boundaries. The autonomous controller intervenes if the intersection is non-empty [248], and the con- trol inputs are calculated using the standard Model Predictive Control (MPC) tech- nique [47].

The driver-controller performance is evaluated in the second hour of the simulated driving experiment. The key result is that at a medium clustering setting, the semi- autonomous controller intervenes 93% of the instances when the driver is in danger; 71% of times that the controller chooses to intervene, the driver is going to be in danger in a near future time window. The semi-autonomous controller keeps the vehicle safe during the entire testing period for all drivers.

Remarks. Their technique enables validating the safety of a semi-autonomous driving system by incorporating a driver behavior model. The proposed framework is modular, i.e., the driver and controller are explicitly modeled as separate compo-

Figure 2.5: The system-level view of a behavior model.

nents, and thus it allows the use of more complex driver and/or control models. One limitation is that the semi-autonomous controller is designed and tested on exactly the same group of drivers. As a result, any possible selection bias is unlikely to be revealed in the testing, i.e., the semi-autonomous controller may fail to handle certain situations that are not observed on those recruited into the study. Addition- ally, the approach does not account for the “behavioral feedback”, i.e., drivers may behave differently when they know there is a semi-autonomous controller acting as a safety backup. In the experiments of the surveyed work, the drivers know whether the controller is activated, and many drivers admit that the controller changes their own behaviors [248]. This is a critical issue in HAI: The automation may impact human psychology and make them more willing to engage in aggressive control ac- tions. The problem is also noted by researchers in other HiL application domains, e.g., the closed-loop medical devices [222].