Probabilistic decision making for collision avoidance systems: Postponing decisions

Abstract

For collision avoidance systems to be accepted by human drivers, it is important to keep the rate of unnecessary interventions very low. This is challenging since the decision to intervene or not is based on incomplete and uncertain information. The contribution of this paper is a decision making strategy for collision avoidance systems which allows the system to occasionally postpone a decision in order to collect more information. The problem is formulated in the framework of statistical decision theory, and the core of the algorithm is to run a preposterior analysis to estimate the benefit of deciding with the additional information. A final decision is made by comparing this benefit with the cost of delaying the intervention. The proposed approach is evaluated in simulation at a two-way stop road intersection for stop sign violation scenarios. The results show that the ability to postpone decisions leads to a significant reduction of false alarms and does not impair the ability of the collision avoidance system to prevent accidents.

Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

“…Lastly, the value of t f from Equation (17) also needs to be checked to ensure it is positive. Figure 3 shows the plot of Equation (19) and the optimum. It can be seen that the equation is highly non-linear with several roots.…”

“…However, it can be seen that the chosen root is very close to the initial guess and can be easily obtained using a simple gradient descent search with a suitable initial guess. Figure 4 shows the optimal paths corresponding to the solutions of Equation (19). As can be seen, even though there are multiple solutions, only one is of interest (φ = 112 • ).…”

“…Other work exists that deal with decision making for such accidents using model-based methodology, probabilistic methods, etc. [15][16][17][18][19][20][21][22]. However these use simplified motion models and/or simple manoeuvres (braking or steering only as opposed to combined braking and steering) to perform their analysis.…”

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

“…The smoothness and consistency of a path are the two key parameters impacting DC in a multi-agent autonomous driving technique 130 . An uneven road may cause occupant discomfort or even wheel slippage, reducing the vehicle's stability.…”