Considering Perspectives for Automated Driving Ethics: Collective Risk in Vehicular Motion Planning

TL;DR

It is concluded that to facilitate ethicality in road traffic that includes AVs, the risk-perspective of each road user must be considered in the decision-making of AVs.

Abstract

Recent automated vehicle (AV) motion planning strategies evolve around minimizing risk in road traffic. However, they exclusively consider risk from the AV's perspective and, as such, do not address the ethicality of its decisions for other road users. We argue that this does not reduce the risk of each road user, as risk may be different from the perspective of each road user. Indeed, minimizing the risk from the AV's perspective may not imply that the risk from the perspective of other road users is also being minimized; in fact, it may even increase. To test this hypothesis, we propose an AV motion planning strategy that supports switching risk minimization strategies between all road user perspectives. We find that the risk from the perspective of other road users can generally be considered different to the risk from the AV's perspective. Taking a collective risk perspective, i.e., balancing the risks of all road users, we observe an AV that minimizes overall traffic risk the best, while putting itself at slightly higher risk for the benefit of others, which is consistent with human driving behavior. In addition, adopting a collective risk minimization strategy can also be beneficial to the AV's travel efficiency by acting assertively when other road users maintain a low risk estimate of the AV. Yet, the AV drives conservatively when its planned actions are less predictable to other road users, i.e., associated with high risk. We argue that such behavior is a form of self-reflection and a natural prerequisite for socially acceptable AV behavior. We conclude that to facilitate ethicality in road traffic that includes AVs, the risk-perspective of each road user must be considered in the decision-making of AVs.

Figures (6)

• Figure 1: (a) Schematic of a typical traffic scene featuring asymmetric risk. First, the aspect of asymmetric uncertainty is depicted by the colored areas. Upper subfigure: the blue vehicle has the white vehicle well in its sight (denoted by its white/grey perception cone); however, it has limited perception (magenta colored cone) of the cyclist, as the cyclist approaches the blue vehicle from its side. Vice-versa in the lower subfigure: the white vehicle has the cyclist well in its forward sight; however, the blue vehicle is in its blind-spot. The cyclist's perception is not depicted, though, clearly, it has both vehicles well in its sight. Second, the asymmetric severity can also be deducted. Here, the blue vehicle could collide with its front into the white vehicle's driver-side door. Such a collision is typically associated with more harm to the white vehicle's passengers than to the passengers of the blue vehicle. Likewise, if the cyclist collides with the blue vehicle, the consequences of the collision would be much more severe for the cyclist than for the blue vehicle's passengers. (b) Example for the assumptions: In the upper subfigure, the ego predicts the uncertain trajectories of two objects, displayed for two different time instances. In the lower subfigure, the ego is estimating the uncertainty both objects have about the planned trajectory of the ego vehicle. The wider distributions for object $1$ indicate that the ego assess that object $1$ is more uncertain about the ego's trajectory than object $2$. (c) Simulation setup and recording of evaluation metrics for a given scenario with an egoistic risk perspective. The user sets the selector, and a scenario is simulated with that selector setting. While only one risk cost is selected to generate ego behavior, all other risk costs are still recorded (see also Figure \ref{['fig:avg_risk_zip']}). Note that the ego's behavior is unaffected by the objects' risks exclusively for the egoistic risk perspective.
• Figure 2: Examples for different risk perspectives (upper subfigures, scenario ZAM_Tjunction-1_25_T-1) and uncertainty settings given a collective risk perspective (lower subfigures, scenario ZAM_Zip-1_49_T-1). The blue vehicle represents the ego vehicle. The fading colors of vehicles denote a time difference of 0.3s in ZAM_Tjunction and 0.2s in ZAM_Zip. The predictions for a prediction horizon of 2s at the presented time step are highlighted by the yellow-to-red ellipses, where a darker color denotes a higher probability. Lastly, the black trajectories of each object denote the respective true trajectory for the prediction horizon, and the blue trajectory shows the planned ego trajectory for the prediction horizon.
• Figure 3: (a) Collective average risk cost for each risk mode (normalized to different scales for visibility). (b) Differences in risk cost allocation between the collective and egoistic perspectives. Negative values indicate a reduction. (c) Histograms of average accumulated collective risk cost at the last time step for ZAM_Tjunction, where, upper row: low uncertainty, center row: moderate uncertainty, bottom row: high uncertainty. Each plot is scaled to contain 18 bins. Note that as we report the average accumulated collective risk cost for each risk-perspective, we obtain nine cases since the uncertainty level always affects the collective risk cost.
• Figure 4: Average accumulated risk costs across scenario clusters, risk perspectives, and uncertainty levels. (a) Average accumulated risk costs over all simulation time steps $k$ for the scenario ZAM_Tjunction. (b) Average accumulated risk costs for the final time step $k=N_T$ for the scenarios ZAM_Zip (left) and USA_US101 (right).
• Figure 5: An ego vehicle and object $o$ covering of three circles each. As an example, we depict three severity functions $s_{j,l}$ for a collision between the front ego circle $j = 1$ with each object circle $l$. We omit time indexing for clarity.