The effects of various auditory takeover requests: A simulated driving study considering the modality of non-driving-related tasks.

With the era of automated driving approaching, designing an effective auditory takeover request (TOR) is critical to ensure automated driving safety. The present study investigated the effects of speech-based (speech and spearcon) and non-speech-based (earcon and auditory icon) TORs on takeover performance and subjective preferences. The potential impact of the non-driving-related task (NDRT) modality on auditory TORs was considered. Thirty-two participants were recruited in the present study and assigned to two groups, with one group performing the visual N-back task and another performing the auditory N-back task during automated driving. They were required to complete four simulated driving blocks corresponding to four auditory TOR types. The earcon TOR was found to be the most suitable for alerting drivers to return to the control loop because of its advantageous takeover time, lane change time, and minimum time to collision. Although participants preferred the speech TOR, it led to relatively poor takeover performance. In addition, the auditory NDRT was found to have a detrimental impact on auditory TORs. When drivers were engaged in the auditory NDRT, the takeover time and lane change time advantages of earcon TORs no longer existed. These findings highlight the importance of considering the influence of auditory NDRTs when designing an auditory takeover interface. The present study also has some practical implications for researchers and designers when designing an auditory takeover system in automated vehicles.

Simulation of vegetation cover on sidewalks in Denmark.

Data from five 3-year experiments comparing different strategies of non-chemical weed control were analysed in order to set up a model that could be used for simulating further strategies. The treatments included different frequencies and effects of weed control using flaming, steaming and weed brushing. Each plot consisted of about 100 m of sidewalk. The amount of weed on the sidewalks was recorded several times during the 3-year period using a system of cameras mounted on a Terrain Vehicle moving on the sidewalk and post processing using image analyses of extracted images. The percentage of sidewalk covered by weed was estimated on the basis of the image analyses. The model used for describing the weed coverage comprises three components. The first component describes the growth of weeds; the second describes reduction caused by wearing and decay of weeds in the autumn and the third describes the reduction caused by weed control using weed brushing, steaming and flaming. The parameters of the model were estimated from the data for later use for simulating additional strategies.

Strategies for non-chemical weed control on public paved areas in Denmark.

To be proactive in minimizing pesticide use, the public authorities in Denmark agreed in 1998 to phase out the use of pesticides on publicly owned areas by the end of 2002. A part of the agreement was an increasing focus on research into and development of new methods and implements for non-chemical weed control on paved areas. Due to a large increase in the costs of non-chemical weed control, the park authorities have to put the different types and locations of paved areas in order of priority to optimize the weed control effort. The present authors divided the paved areas into five weed control levels, dependent on placement, quality and use. For the 3 years 1999-2001, experiments with different non-chemical weed control methods were conducted on pavements at six locations in Denmark. The aim was to test the reaction of the weeds to different treatments and strategies. The efficacies of the methods were evaluated by analysis of digital images to estimate the fraction of the paved area covered with green vegetation (weed coverage). The weed coverage was used as the dependent variable in the subsequent statistical analysis. The independent variables in the model were incoming radiation, wear, area of joints in the pavement, the dying process of the weeds and the number of runs/applied energy of the mechanical or thermal weed control methods, respectively. The estimated parameters from the statistical model were used to build a simulation model, which was used to optimise five weed control strategies to fulfil the suggested weed control levels. In the suggested strategy for maximum weed control, 12 thermal weeding applications at 2-week intervals are suggested. The 'clean-up' strategy is based on one weed-brushing in late spring or early autumn.