Automated Driving and its Effect on the Safety Ecosystem: How do Compatibility Issues Affect the Transition Period?

AbstractDifferent components of automated vehicles are being made available commercially as we speak. Much research has been conducted into these components and many of these have been studied with respect to their effects on safety, but the transition period from non-automated driving to fully automated vehicles raises safety related issues dealing with mixed traffic situations. More in-depth knowledge should be gained in (the safety of) the behaviour of drivers of unequipped vehicles, enabling automated vehicles to predict and adequately respond to potentially unsafe behaviour, a concept we call backwards compatibility. Also, automated vehicle system design tends to be from an optimal system performance perspective which leads to driving patterns such as driving in the centre of a lane. Other (human) road users however likely exhibit driving behaviour in line with different rationales which allow for suboptimal driving patterns. As of yet, it remains unclear whether these patterns contain indications about the intentions of a driver and if or how other road users anticipate these. This could have two consequences with regard to mixed traffic situations. First of all, other road users might miss important cues from the behaviour of the automated vehicle (what we call forward incompatibility). Secondly, the occupant of an automated vehicle might expect human-like behaviour from the automated vehicle in safety-critical situations, lowering acceptance if this does not meet expectations. The current paper considers these issues and states that we need more insight in how road users use other road users’ behaviour to anticipate safety critical events, especially in the transition period towards fully automated vehicles

The VMC ESO Public Survey

The VISTA near-infrared YJKs survey of the Magellanic Clouds system (VMC) has entered its core phase: about 50% of the observations across the Large and Small Magellanic Clouds (LMC, SMC), the Magellanic Bridge and Stream have already been secured and the data are processed and analysed regularly. The initial analyses, concentrated on the first two completed tiles in the LMC (including 30 Doradus and the South Ecliptic Pole), show the superior quality of the data. The photometric depth of the VMC survey allows the derivation of the star formation history (SFH) with unprecedented quality compared to previous wide-area surveys, while reddening maps of high angular resolution are constructed using red clump stars. The multi-epoch Ks-band data reveal tight period-luminosity relations for variable stars and permit the measurement of accurate proper motions of the stellar populations. The VMC survey continues to acquire data that will address many issues in the field of star and galaxy evolution

Drowsy drivers' under-performance in lateral control:how much is too much? Using an integrated measure of lateral control to quantify safe lateral driving

\u3cp\u3eInternationally, drowsy driving is associated with around 20% of all crashes. Despite the development of different detection methods, driver drowsiness remains a disconcerting public health issue. Detection methods can estimate drowsiness by directly measuring the physiology of the driver, or they can measure the effect that drowsiness has on the state of the vehicle due to the behavioural changes that drowsiness elicits in the driver. The latter has the benefit that it could measure the net effect that drowsiness has on driving performance which links to the actual safety risk. Fusing multiple sources of driving performance indicators like lane position and steering wheel metrics in order to detect drowsiness has recently gained increased attention. However, not much research has been conducted with regard to using integrated measures to detect increased drowsiness within an individual driver. Different levels of drowsiness are also rarely classified in terms of safe or unsafe. In the present study, we attempt to slowly induce drowsiness using a monotonous driving task in a simulator, and fuse lane position and steering wheel angle data into a single measure for lateral control performance. We argue that this measure is applicable in real-time detection systems, and quantitatively link it to different levels of drowsiness by validating it to two established drowsiness metrics (KSS and PERCLOS). Using level of drowsiness as a surrogate for safety we are then able to set simple criteria for safe and unsafe lateral control performance, based on individual driving behaviour.\u3c/p\u3