The Planning Process of Transport Tasks for Autonomous Vans—Case Study
Abstract
:1. Introduction
2. Artificial Intelligence in Vehicles
3. Route Planning and Control Algorithms
- The division of the set of all points reporting the need for service or destinations into regions, each of which will be assigned to one vehicle.
- Determining the order of service within the area.
4. Modeling Example
- E0—all vehicles free, no tasks to be serviced,
- E1—one vehicle busy, one notification in the system,
- E2—two vehicles seized, two reports in the system.
- Em—m of vehicles occupied, m notification in the system,
- Ej—m of vehicles occupied, j-m reports in the queue for service,
- EN—m of vehicles occupied, N points waiting for service, N-m requests waiting for service.
- α(t) is the profit at time t;
- δ—unit cost of maintaining the vehicle at a single moment (we assumed the value 1);
- m—number of vehicles;
- μ—notification service intensity (such part of the entire notification is processed in a single step by a single vehicle);
- β—profit resulting from a fully handled request (500 was assumed);
- p(t)—system status, number of notifications in the system;
- min(m, p(t))—the number of notifications handled at the moment (if p(t) ≥ m, then only m notifications are handled because we have only m vehicles; if p(t) < m, all notifications are supported);
- λ—intensity of the appearance of a new notification (such a part of the cost of not handling the notification is incurred in a single step);
- γ—profit resulting from a completely unhandled report (200 was assumed);
- max(0, p(t)–m)—the number of unhandled reports at the moment (if m ≥ p(t), then all reports are handled; if m < p(t), then there are unsupported reports in the system p(t)–m tickets).
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | |
---|---|
Standard deviation res. | σ = 0.05234 t |
Shapiro–Wilks test of normality of the residues | (the residues do not have a normal distribution). |
The fit factor | R2 = 0.8615 |
Corrected alignment factor | R2 = 0.8614 (86.14% of the variation θ is explained by regression). |
Fisher’s statistics of quality of fit | (the model is good). |
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Caban, J.; Nieoczym, A.; Dudziak, A.; Krajka, T.; Stopková, M. The Planning Process of Transport Tasks for Autonomous Vans—Case Study. Appl. Sci. 2022, 12, 2993. https://doi.org/10.3390/app12062993
Caban J, Nieoczym A, Dudziak A, Krajka T, Stopková M. The Planning Process of Transport Tasks for Autonomous Vans—Case Study. Applied Sciences. 2022; 12(6):2993. https://doi.org/10.3390/app12062993
Chicago/Turabian StyleCaban, Jacek, Aleksander Nieoczym, Agnieszka Dudziak, Tomasz Krajka, and Mária Stopková. 2022. "The Planning Process of Transport Tasks for Autonomous Vans—Case Study" Applied Sciences 12, no. 6: 2993. https://doi.org/10.3390/app12062993
APA StyleCaban, J., Nieoczym, A., Dudziak, A., Krajka, T., & Stopková, M. (2022). The Planning Process of Transport Tasks for Autonomous Vans—Case Study. Applied Sciences, 12(6), 2993. https://doi.org/10.3390/app12062993