The objective of the study is to quantify the benefits of an earlier brake activation by the drivers potentially achieved by a Forward Collision Warning (FCW) system in simulated car-to-cyclist accident scenarios. A parametric analysis is performed by varying the detection sensor Field Of View (FOV), the FCW trigger time and the driver's reaction lag time to the FCW. Almost two thousand and three hundred car-to-cyclist accidents are clustered in the following five main scenarios: crossing nearside (33%), crossing farside (22%), longitudinal (5%), turning left (12%) and turning right (22%). The remaining is clustered in Others group (6%). For all accident cases, original accident kinematics are processed through Matlab scripts from which FCW FOV, FCW trigger time and driver's reaction can be modified. The Matlab scripts return the new accident kinematics which can result in the accident being avoided or mitigated. This study shows that a 70° FOV, a FCW trigger time of 2.6s before the impact and a 0.6s driver's reaction to the FCW has a positive result in 82% of the accident cases with 78% being avoided and 4% mitigated. Concerning the parameters, the FOV has a greater influence on the avoidance rates compared to FCW trigger time and driver's reaction. Our study also reveals that FCW system has a higher benefit in the crossing farside scenario and a lower benefit in the turning right scenario. This paper highlights generic characteristics of FCW systems to optimize safety benefit for the different accident scenarios.

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Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers' acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT ("Proactive Safety for Pedestrians and Cyclists"). Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4 s (it takes the cyclist 4 s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6 s (asymptotic value of the model at TTA ¼ 4 s) and the warning outside the CB was at TTC ¼ 1.7 s (below the lower 95% value at TTA ¼ 4 s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey. Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models. Conclusions: Similar to other studies' results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers. ARTICLE HISTORY

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving-related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car-following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Event-related potentials (ERPs) time-locked to decision outcomes are reported. Participants engaged in a gambling task (see [1] for details) in which they decided between a risky and a safe option (presented as different coloured shapes) on each trial (416 in total). Each decision was associated with (fully randomised) feedback about the reward outcome (Win/Loss) and its magnitude (varying as a function of decision response; 5–9 points for Risky decisions and 1–4 points for Safe decisions). Here, we show data demonstrating: (a) the influence of Win feedback in the preceding outcome (Outcomet−1) on activity related to the current outcome (Outcomet); (b) difference wave analysis for outcome expectancy- separating Expected Outcomes (consecutive Loss trials subtracted from consecutive reward) from Unexpected Outcomes (subtracting Losst−1Wint trials from Wint−1Losst trials); (c) difference waves separating Switch and Stay responses for Outcome Expectancy; (d) the effect of magnitude induced by decisions (Riskt vs. Safet) on Outcome Expectancy; and finally, (e) expectations reflected by response switch direction (Risk to Safe responses vs. Safe to Riskt) on the FRN at Outcomet.