Model-based probabilistic collision detection in autonomous driving
IEEE Transactions on Intelligent Transportation Systems
Results of a precrash application based on laser scanner and short-range radars
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Situation assessment for automatic lane-change maneuvers
IEEE Transactions on Intelligent Transportation Systems
Model-based threat assessment for avoiding arbitrary vehicle collisions
IEEE Transactions on Intelligent Transportation Systems
A fuzzy aid rear-end collision warning/avoidance system
Expert Systems with Applications: An International Journal
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This paper deals with the problem of decision making in the context of forward collision mitigation system design. The authors present a multilevel collision mitigation (CM) approach that allows a flexible tradeoff between potential benefit and the risk associated with driver acceptability and product liability. Due to its practical relevance, algorithms that allow for an efficient incorporation of both sensor and prediction uncertainties are further outlined. The performance tradeoffs that come along with different parameterizations are investigated by means of stochastic simulations on three dangerous traffic situations, namely 1) rear-end collisions due to an unexpected braking, 2) cutting-in vehicles, and 3) crossing traffic at intersections. The results show that an overly conservative CM system sacrifices much of its potential benefit. However, it is pointed out that the vision of accident-free driving can be achieved only through cooperative driving strategies