This study was designed to investigate the relative accident risk of different road weather conditions and combinations of conditions. The study applied a recently developed method which is based on the notion of Palm probability, originating in the theory of random point processes, which in this case corresponds to picking a random vehicle from the traffic. The method consists of calculating the Palm distribution of different conditions and comparing it with the distribution of the same conditions as seen by the accidents. The condition affects the accident risk statistically, when these two distributions differ. The study included all police reported single- and multi-vehicle accidents (N = 10,646) occurring on 43 main roads in Finland during the years 2014-2016. A major contribution of this paper is the demonstration of the method on national scale by using estimated hourly traffic volumes on road segments instead of measured ones, which would have been available for few roads only. Accident risks are commonly examined in relation to traffic volume. This paper includes the speed of the traffic and thus, the paper examines accident risk in relation to the time spent on the road segment in certain conditions. The hour-level weather and road condition data per segment were obtained from nearby road weather stations. The relative accident risks were increased for poor road weather conditions; however, they were highest for icy rain and slippery and very slippery road conditions. When comparing the relative accident risk based on road type, the results showed that the risk in poor weather and road conditions was higher on motorways compared to two-lane and multiple-lane roads even though the overall risk was lower on motorways. Furthermore, the corresponding relative accident risks were generally higher for single-vehicle accidents compared to multi-vehicle accidents.

Over the past decade a large number of Field Operational Tests (FOT) have been conducted to test Intelligent Transport Systems (ITS) in real traffic conditions with thousands of drivers. In order to ensure scientifically sound studies a FOT methodology was developed in the FESTA project. Currently we are on the brink of a new series of large scale FOTs, testing automated and autonomous vehicles. A common FOT methodology serves the following purposes: (1) to ensure that a systematic and scientific approach is taken by FOTs, (2) to enable the assessment of the impact of large-scale introduction of ITS on safety, mobility, efficiency and environment, (3) to be able to compare results of different FOTs, and (4) to build a community and facilitate knowledge exchange. FESTA focuses strongly on the drivers of vehicles, and the changes in their behaviour when driving a vehicle that is instrumented with new systems. In FESTA, it is recommended that driving with an ITS is compared with driving without it (the baseline). However, what will be the focus of the new FOTs? And what will be the main research questions these FOTs will address? And what is the baseline? Three types of focus can be distinguished; centred on the user, the vehicle or the context. In this paper we discuss the requirements for a methodology that addresses these three types of focus. We investigate how the current FOT methodology may be adapted or may need to be completely changed. Special attention is given to the type of data that is needed for baselines and for answering research and impact questions

A number of studies have investigated the acceptance of conditionally automated cars (CACs). However, in the future, CACs will comprise of several separate Automated Driving Functions (ADFs), which will allow the vehicle to operate in different Operational Design Domains (ODDs). Driving in different environments places differing demands on drivers. Yet, little research has focused on drivers’ intention to use different functions, and how this may vary by their age, gender, country of residence, and previous experience with Advanced Driving Assistance Systems (ADAS). Data from an online survey of 18,631 car drivers from 17 countries (8 European) was used in this study to investigate intention to use an ADF in one of four different ODDs: Motorways, Traffic Jams, Urban Roads, and Parking. Intention to use was high across all ADFs, but significantly higher for Parking than all others. Overall, intention to use was highest amongst respondents who were younger (<39), male, and had previous experience with ADAS. However, these trends varied widely across countries, and for the different ADFs. Respondents from countries with the lowest Gross Domestic Product (GDP) and highest road death rates had the highest intention to use all ADFs, while the opposite was found for countries with high GDP and low road death rates. These results suggest that development and deployment strategies for CACs may need to be tailored to different markets, to ensure uptake and safe use.

The L3Pilot project tested SAE Level 3 (L3) conditionally automated driving functions addressing driving and travel behavior, impacts on safety, efficiency, environment and socio-economics, and user acceptance. To investigate individual variance in acceptance of conditionally automated cars, an online survey was performed among 18,631 respondents from 17 countries evaluating differences in age, gender, knowledge about the functionality of conditionally automated cars, awareness, information consumption behavior, and expected benefits of conditionally automated cars. Respondents were divided into Enthusiasts, Neutrals, and Sceptics differing in a high, moderate, and low acceptance of conditionally automated cars, respectively. Enthusiasts, Neutrals, and Sceptics differed most with regard to the expected benefits in the productive use of travel time, comfort, and safety of conditionally automated cars. Enthusiasts were male, younger, more knowledgeable about conditionally automated cars, more aware of automated cars, and more likely to receive information about automated cars from different sources, expecting improvements in the productive use of travel time, comfort, and safety due to conditionally automated cars. All groups were most knowledgeable about the lane keeping behavior of conditionally automated cars and least knowledgeable about the operation of conditionally automated cars in dedicated operational design domains. The results indicate that the communication and marketing of automated cars should create a realistic image of the capabilities and limitations of conditionally automated cars where user education programs should be harmonized to calibrate expectations and educate the public.