Airspace Encounter Models for Estimating Collision Risk

By Mykel J. Kochenderfer, M. Edwards, L. Espindle, J. Kuchar, J. Griffith. 2010

170 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Safety, Risk, Reliability and Quality

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Clark W. Barrett
2. Guy Katz
3. David L. Dill
4. Kyle D. Julian
5. Mykel J. Kochenderfer

In Artificial Intelligence

1. Clark W. Barrett
2. Guy Katz
3. David L. Dill
4. Mykel J. Kochenderfer
5. Kyle D. Julian
6. Jessica Lopez
7. Michael P. Owen
8. J. Brush

Citations in the corpus

• Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (2017, background): “5 million simulated encounters [20], but this is insufficient for proving that faulty behaviors do not exist within the continuous DNNs.”
• Policy compression for aircraft collision avoidance systems (2016, methodology): “To quantify the performance of the compressed score representations against the performance of the original table, the neural network code was integrated into a framework for simulating aircraft encounters generated by an airspace encounter model [22].”