The introduction of autonomous vehicles is expected to have a significant impact on the traffic flows at currently traffic light controlled intersections. Recent studies show that the effective capacity of intersection can be maximized. Automated Intersection Management controls traffic using individual time-space reservations. Autonomous vehicles are able to cross the intersection in a ‘woven’ manner. Optimization methods will make it possible to approach the highest theoretical capacity of an intersection: 100% car coverage of the intersection at the highest speed possible.
Autonomous Intersection Management is only possible in a fully autonomous environment. As long as there are ‘legacy’ vehicles on the road and vulnerable road users (pedestrians and cyclists) make use of the intersection, this most efficient control of intersections is not possible without compromising road safety. The challenge arises how the effective capacity of the intersection can be maximized making use of the possibilities of autonomous vehicles in a hybrid environment.
The current state-of-the-art traffic light controllers are connected and communicate with connected vehicles, which have either a connected on-board device or a mobile phone with a specific app. Approaching vehicles may communicate their current position, speed and even route to the controller (DAMN, CAM), which uses this additional information to optimize its control. On the other hand, the controller broadcasts the scheduled green phases and intersection topology (SPaT/MAP) to all vehicles in its surround.
In this paper we introduced a new message-protocol: the individual Space & Time message (iSPaT). For each approaching vehicle the controller determines a time-space reservation. This reservation can be considered as a timeslot on a specific lane at which the vehicle may cross the intersection without encountering other vehicles or road users. In the hybrid environment the scheduled timeslots are within regular green phases, meaning communication from the controller to the road users happens in three ways: with regular lights, with the SPaT-messages and with the iSPaT-messages.
When the penetration rate of autonomous vehicles increases, the traffic controller shifts more and more from Hybrid Intersection Management towards Autonomous Intersection Management. In this paper we proposed a concept which uses the same technologies with various penetration rates of autonomous vehicles. Our proposed hybrid controller makes it possible to benefit from an improved traffic flow and air quality even with low number of autonomous vehicles, and is able to process larger amounts of autonomous vehicles.
The development of Hybrid Intersection Management can be started today, making intersections ready for the first (semi-)autonomous vehicles. Benefits of Automatic Intersection Management, like improved traffic flows and air quality, can already be gained. When the number of autonomous vehicles increases the proposed controller slightly shifts from hybrid to automatic.
- David Fajardo, Tsz-Chiu Au, S. Travis Waller, Peter Stone, and David Yang (2012). Automated Intersection Control: Performance of a Future Innovation Versus Current Traffic Signal Control. In Transportation Research Record : Journal of the Transportation Research Board.
- K. Dresner and P. Stone. Multiagent traffic management: A reservation-based intersection control mechanism. In The Third International Joint Conference on Autonomous Agents and Multiagent Systems, pages 530–537, New York, NY, USA, July 2004.
- K. Dresner and P. Stone. Multiagent traffic management: An improved intersection control mechanism. In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 471–477, Utrecht, The Netherlands, July 2005.
- Remi Tachet, Paolo Santi, Stan Sobolevsky, Luis Reyes-Castro, Emilio Frazzoli, Dirk Helbing, Carlo Ratti (2016). Revisiting Street Intersections using Slot-Based Systems. PloS ONE.
- Guni Sharon and Peter Stone (2017). A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections. In Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Best Papers.
- J. Wahlstedt. Evaluation of the two self-optimising traffic signal systems Utopia/Spot and ImFlow, and comparison with existing signal control in Stockholm, Sweden. In Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, the Netherlands, October 6-9 2013, pp. 1541-1546.
- Robertson, D. I. (1969). TRANSYT: a traffic network study tool.
- Mauro, V., Di Taranto, C. (1987). UTOPIA, CCCT89-AFCET Proceedings, Paris
- Hunt, P.B., Robterson, D.O., Bretherton, R.D., Winton, R.D. (1981). SCOOT, A Traffic Responsive Mehtod of Coordinating Signals, TRRL Laboratory Report 1014
- J. Santa, F. Pereñíguez, A. Moragón and A. F. Skarmeta. Vehicle-to-infrastructure messaging proposal based on CAM/DENM specifications. In 2013 IFIP Wireless Days (WD), Valencia, 2013, pp. 1-7
- Van der Bijl, B. and Brouwer, J. (2018). Predictive Traffic Management Services, 25th ITS World Congress, Copenhagen, Denmark.
- A. Festag, Cooperative intelligent transport systems standards in Europe. In IEEE Communications Magazine, vol. 52, no. 12, pp. 166-172, December 2014.
Senior consultant Smart Mobility
BAS VAN DER BIJL