Modelling, State Observation, and Diagnosis of Quantised Systems
Modelling, State Observation, and Diagnosis of Quantised Systems
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational methods for reachability analysis of stochastic hybrid systems
HSCC'06 Proceedings of the 9th international conference on Hybrid Systems: computation and control
Aircraft conflict prediction in the presence of a spatially correlated wind field
IEEE Transactions on Intelligent Transportation Systems
ATMS implementation system for identifying traffic conditions leading to potential crashes
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Real-Time Traffic Simulation With a Microscopic Model
IEEE Transactions on Intelligent Transportation Systems
Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems
IEEE Transactions on Intelligent Transportation Systems
Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling
IEEE Transactions on Intelligent Transportation Systems
Sensor and actuator fault diagnosis of systems with discrete inputs and outputs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Toward autonomous vehicle safety verification from mobile cyber-physical systems perspective
ACM SIGBED Review - Work-in-Progress (WiP) Session of the 2nd International Conference on Cyber Physical Systems
Hi-index | 0.00 |
The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.