Effects of Non-Driving Related Tasks During Self-Driving Mode

Abstract

Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.

Integration of Wearables and Wireless Technologies to Improve the Interaction between Disabled Vulnerable Road Users and Self-Driving Cars

“…However, based on our preliminary results, we can conclude that it is feasible to equip a D-VRU with an IEEE 802.11p-based device to give them confidence in their daily mobility routines, allowing them to safely move around the city, crossing avenues with high and moderate traffic. Furthermore, according to Minhas, et al [78], human drivers take an average of 3.15 s to put their hands back on the steering wheel when a vehicle is self-driving and the driver is distracted, and an average of 2.47 s to put their feet on the pedals. We believe that using the mechanisms defined in this paper can significantly reduce this time, as the system's reaction time is negligible when compared with human drivers.…”

Effects of non-driving related tasks during autonomous driving

“…v podobě prodloužení reakčního času, kratšího TTC, výskytu většího množství kolizí apod. (Minhas et al, 2022;Dogan et al, 2019;Gold et al, 2017;Wu et al, 2019;Soares et al, 2021). Shanini & Zahabi (2021) ve své meta analýze zjistili, že řízení bez neřidičské činnosti vede k lepšímu výkonu, konkrétně v podobě kratšího času položení rukou na volant, kratšího brzd ného času, delšího TTC a počtu nehod ve srovnání s řízením s neřidičskou akti vitou.…”

“…v podobě prodloužení reakčního času, kratšího TTC, výskytu většího množství kolizí apod. (Minhas et al, 2022;Dogan et al, 2019;Gold et al, 2017;Wu et al, 2019;Soares et al, 2021;Vogelpohl et al, 2019;Yoon et al, 2019).…”

Weather Classification by Utilizing Synthetic Data

“…This is the main reason why the use of synthetic data can be more productive as compared to the real-world counterpart. In this paper, there are two main objectives: the first is to assess the modifications made to the driver simulator, which was previously used for driver vehicular interactions [24]. The second objective is to test the performance of the generated dataset with other comparable groundtruth datasets.…”