Tom Alkim

Driving behavior interaction with ACC: results from a Field Operational Test in the Netherlands

In the Netherlands, a field operational test was conducted in 2006 to assess the impact of two advanced driver assistance systems (ADAS), namely adaptive cruise control (ACC) and lane departure warning (LDW) systems. The research goal was to estimate the effects of these systems on road capacity, safety and emissions. In this paper we focus on the interaction between driver and ACC system using the data from this FOT. It is found that drivers choose headway settings according to their manual driving behavior. Moreover, they often keep the system deactivated under dense traffic conditions. It is also observed from the data that the system, once de-activated, either automatically or manually, needs some time to become again active. These findings imply that, even with 100% cars on the road equipped with ACC, manual driving behavior will still be a determinant factor.

A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays

Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.

Changes in Trust after Driving Level 2 Automated Cars

Overtrust and undertrust are major issues with partially automated vehicles. Ideally, trust should be calibrated ensuring that drivers’ subjective feelings of safety match the objective reliability of the vehicle. In the present study, we examined if drivers’ trust toward Level 2 cars changed after on-road experience. Drivers’ self-reported trust was assessed three times: before having experience with these vehicles, immediately after driving two types of vehicles, and two weeks after the driving experience. Analysis of the results showed major changes in trust scores after the on-road driving experience. Before experiencing the vehicles, participants tended to overestimate the vehicle capabilities. Afterwards they had a better understanding of vehicles’ limitations, resulting in better calibrated trust.

The Effect of See-Through Truck on Driver Monitoring Patterns and Responses to Critical Events in Truck Platooning

Automated platooning of trucks has its beneficial effects on energy saving and traffic flow efficiency. The vehicles in a platoon, however, need to maintain an extremely short headway to achieve these goals, which will result in a heavily blocked front view for the driver in a following truck. Monitoring surrounding traffic environment and foreseeing upcoming hazardous situations becomes a difficult, yet safety-critical task. This exploratory study aims to investigate whether providing platoon drivers with additional visual information of the traffic environment can influence their monitoring pattern and increase awareness of the upcoming situation. 22 professional truck drivers participated in the driving simulator experiment, either following a see-through lead truck (i.e., with projection of forward scene attached to the rear of the lead truck), or a normal lead truck until the automation system failed unexpectedly in a critical situation. Results showed that when provided with front view projection, the participants spent 10% more time monitoring the road, and responded less severely to a critical situation, suggesting a positive effect of the “see-through” technology.