Autonomous Driving Effects on Sustainable Transportation (ADEST)
|Coordinator||Volvo Personvagnar Aktiebolag - Avd 96000, PVV|
|Funding from Vinnova||SEK 90 000 000|
|Project duration||September 2014 - October 2019|
|Venture||FFI - Board of directors initiated project|
|End-of-project report||2014-06012 (DriveMe).pdf (pdf, 2014 kB)|
Purpose and goal
The purpose of the project is to define and evaluate the impact of automated vehicles on the research areas safety, and traffic efficiency (i.e., capacity, robustness, travel time and punctuality). Additionally, the test probe research area was included with the purpose to develop and implement automated driving functionality in vehicles in order to evaluate the performance on real roads with regular customers. The operational design domain (ODD) is restricted to urban highways on the Gothenburg ring road during daylight and good weather/roadway conditions.
Expected results and effects
Autonomous vehicles (AVs) have a crash avoidance potential that can surpass an attentive, skilled and experienced reference driver model. The traffic flow is marginally affected by introducing a share of cautious AVs in rush hour traffic on large parts of the ring road. In contrast, the queues increase at locations where there currently are capacity problems. The traffic improvement potential by entirely avoiding accidents and incidents suggest that the variations in daily travel time would significantly decrease with a large share of AVs.
Planned approach and implementation
Real-world crashes were reconstructed, and applied in computer simulations to compare the crash avoidance performance of an automated vehicle model to a target defined as an attentive, skilled, and experienced reference driver model. Test track experiments were used to study driver performance in supervised automation. The effect of automated vehicles on traffic (i.e., capacity, travel time and robustness) has been evaluated using: (1) traffic simulations (in VISSIM and SUMO), (2) analytical-empirical methods, and (3) analysis of traffic and accident-incident data.