Name
Abstract | ||
|---|---|---|
Drones can assist in mitigating traffic accidents by deterring reckless drivers, leveraging their flexible mobility. In the real-world, drones are fundamentally limited by their battery/fuel capacity and have to be replenished during long operations. In this article, we propose a novel approach where police cruisers act as mobile replenishment providers in addition to their traffic enforcement duties. We propose a binary integer linear program for determining the optimal rendezvous cruiser-drone enforcement policy, which guarantees that all drones are replenished on time and minimizes the likelihood of accidents. In an extensive empirical evaluation, we first show that human drivers are expected to react to traffic enforcement drones similar to how they react to police cruisers, using a first-of-its-kind human study in realistic simulated driving. Then, we show that our proposed approach significantly outperforms the common practice of constructing stationary replenishment installations using both synthetic and real-world road networks. Finally, we propose and evaluate a novel optimization speedup method for mitigating the increased runtime of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.artint.2019.103166 | Artificial Intelligence |
Keywords | Field | DocType |
Security,Traffic enforcement,Drones,Energy limitation,Rendezvous route planning | Computer security,Computer science,Enforcement,Artificial intelligence,Drone,Machine learning | Conference |
Volume | Issue | ISSN |
277 | 1 | 0004-3702 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ariel Rosenfeld | 1 | 87 | 13.03 |
| Oleg Maksimov | 2 | 18 | 3.05 |
| Sarit Kraus | 3 | 6810 | 768.04 |