Estimated crash avoidance with the hypothetical introduction of automated vehicles: a simulation based on experts’ assessment from French in-depth data

Objective We aimed to quantify, through simulations using real crash data, the number of potentially avoided crashes following different replacement levels of light vehicles by level-5 automated light vehicles (AVs). Methods Since level-5 AVs are not on the road yet, or are too rare, we simulated their introduction into traffic using a national database of all fatal crashes and 5% of injury crashes observed in France in 2011. We fictitiously replaced a certain proportion of light vehicles (LVs) involved in crashes by level-5 AVs, and applied crash avoidance probabilities estimated by a number of experts regarding the capabilities of AVs depending on specific configurations. Estimates of the percentage of avoided crashes per user configuration and according to three selected (10%, 50%, 100%) replacement levels were made, as well as estimates taking into account the relative weight of these crash configurations, and considering fatal and injury crashes separately. Results Our simulation suggests that a reduction of almost half of fatal crashes (56%) and injury crashes (46%) could be expected by replacing all LVs on the road with level-5 AVs. The introduction of AVs would be the least effective for crashes involving a vulnerable road user, especially motorcyclists. Conclusion This result represents encouraging prospects for the introduction of automated vehicles into traffic, while making it clear that, even with all light vehicles replaced with level 5-AVs, all issues would not be solved, especially for crashes involving motorcyclists, cyclists and pedestrians.

Video observation of encounters between the automated shuttles and other traffic participants along an approach to right-hand priority T-intersection

Introduction Increasing numbers of deployment projects of automated shuttles have been taking place worldwide. Safety is one of the main concerns for their successful implementation. Therefore, it is vital to gain the knowledge about interactions between these shuttles and other traffic participants. Method Given the lack of behavioural observational studies under regular traffic conditions, the presented study applies external video recordings to explore encounters between the shuttles approaching a T-intersection and other traffic participants. The encounters of interest included a vulnerable road user in the bicycle lane, a pedestrian on the zebra crossing and a road user overtaking the shuttle. The shuttles were identified from the video by RUBA software. We analysed the encounters using T-Analyst software together with the manual observation of traffic participants' behaviour. Results From 220 h of video, 318 unique manoeuvres of the shuttle were observed and 83 encounters with other traffic participants were identified and explored. Several types of risks and behavioural patterns were identified, such as road users misusing the defensive style of the shuttles or cyclists in the bicycle lane not being sure about the shuttle’s intention. Frequent hard stops of the shuttles might be dangerous for the passengers inside and can increase the risk of rear end accidents. Conclusions The findings provide a valuable insight into the interactions between automated shuttles and other traffic participants under regular traffic conditions on one location in Oslo, Norway. The study showed that introducing automated shuttles into regular traffic can lead to the emergence of new types of interactions between the shuttles and other traffic participants.

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

An Analysis of Vulnerable Road Users Overtaking Maneuver along the Urban Road

This paper discusses driving styles while overtaking a vulnerable road user who moves along the shoulder in urban roads. Based on the data obtained from an experiment in pre-defined conditions (combinations of four main effects: vehicle’s initial speed, lane width of the road, vulnerable road users’ type, and location in the shoulder) with an immersive driving simulator, we analyzed four different driving styles of drivers while approaching and passing the objects. It is shown that drivers took avoidance maneuvers even if there was no clear risk of collision to vulnerable road users. The results showed that the drivers tended to have a unique perception about the lateral passing gap and overtaking strategy with two worth notice groups: overcaution drivers and reckless drivers. The road characteristic has a statistically significant effect for all types of drivers. Moreover, the effect of the vehicle’s initial speed on overtaking strategy and the effect of vulnerable road user location on minimum lateral passing gap are statistically significant. The findings provide some implications for the development of automotive safety systems that can reduce the risk of overtaking maneuvers in urban areas.

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

Identifying urban features for vulnerable road user safety in Europe

One of the targets of the UN Sustainable Development Goals is to substantially reduce the number of global deaths and injuries from road traffic collisions. To this aim, European cities adopted various urban mobility policies, which has led to a heterogeneous number of injuries across Europe. Monitoring the discrepancies in injuries and understanding the most efficient policies are keys to achieve the objectives of Vision Zero, a multi-national road traffic safety project that aims at zero fatalities or serious injuries linked to road traffic. Here, we identify urban features that are determinants of vulnerable road user safety through the analysis of inter-mode collision data across European cities. We first build up a data set of urban road crashes and their participants from 24 cities in 5 European countries, using the widely recommended KSI indicator (killed or seriously injured individuals) as a safety performance metric. Modelling the casualty matrices including road infrastructure characteristics and modal share distribution of the different cities, we observe that cities with the highest rates of walking and cycling modal shares are the safest for the most vulnerable users. Instead, a higher presence of low-speed limited roads seems to only significantly reduce the number of injuries of car occupants. Our results suggest that policies aimed at increasing the modal share of walking and cycling are key to improve road safety for all road users.