Abstract
Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road users to zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project "Digitaler Knoten 4.0" cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes with-in many cases-serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.
Highlights
Automated driving has become more and more realistic in relatively simple traffic environments, it has not been assured to reduce the number of less severe crashes in urban areas in comparison to manual driving (e.g. NHTSA, 2017; Pink et al, 2015)
The interaction behaviour was analysed based on the PET, accepted/non-accepted gaps as well as the distance to arrive at the conflict point and the time needed to arrive at the conflict point for the first acceleration/deceleration manoeuvre of the left turning vehicle
The results showed that the automated driving functions (ADF) of the automated vehicles (AV) had a conservative behaviour conducting the conditionally tolerable left turns at this specific urban intersection: PET values of the AV and manually driven vehicles (MV) groups were on opposite ends of the PET distribution
Summary
Automated driving has become more and more realistic in relatively simple traffic environments (e.g. on motorways), it has not been assured to reduce the number of less severe crashes in urban areas in comparison to manual driving (e.g. NHTSA, 2017; Pink et al, 2015). This, intensified the discussion about the technical degree of maturity of automated driving functions (ADF) and requires the research work ahead The reason for this is that urban areas are complex and one of the greatest challenges for ADF as different road users (motorists, bicyclists, pedestrians, e-scooter drivers, etc.) with different intentions (e.g. commuting, shopping, or leisure time) and different speed meet each other in different traffic areas with different signalling (e.g. traffic lights and traffic signs). Brunner et al (2019) state that this requires more than a billion test kilometres, which is economically unacceptable in a realistic time period This is one of the reasons self-driving cars currently struggle to avoid crashes
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