A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario’s kinematics, quantified in terms of visual looming of the lead vehicle on the driver’s retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5 s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined “hazard onset”, but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario

Nova genera plantarum...

pt.3, 5 1783-8

Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers

Improving the performance of systems is a goal pursued in all areas and vehicles are no exception. In places like Europe, where the majority of goods are transported over land, it is imperative for fleet operators to have the best efficiency, which results in efforts to improve all aspects of truck operations. We focus on drivers and their performance with respect to fuel consumption. Some of relevant factors are not accounted for inavailable naturalistic data, since it is not feasible to measure them. An alternative is to set up experiments to investigate driver performance but these are expensive and the results are not always conclusive. For example, drivers are usually aware of the experiment’s parameters and adapt their behavior. This paper proposes a method that addresses some of the challenges related to categorizing driver performance with respect to fuel consumption in a naturalistic environment. We use expert knowledge to transform the data and explore the resulting structure in a new space. We also show that the regions found in APPES provide useful information related to fuel consumption. The connection between APPES patterns and fuel consumption can be used to, for example, cluster drivers in groups that correspond to high or low performance. © 2017 IEE

Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data

Fuel used by heavy duty trucks is a major cost for logistics companies, and therefore improvements in this area are highly desired. Many of the factors that influence fuel consumption, such as the road type, vehicle configuration or external environment, are difficult to influence. One of the most under-explored ways to lower the costs is training and incentivizing drivers. However, today it is difficult to measure driver performance in a comprehensive way outside of controlled, experimental setting. This paper proposes a machine learning methodology for quantifying and qualifying driver performance, with respect to fuel consumption, that is suitable for naturalistic driving situations. The approach is a knowledge-based feature extraction technique, constructing a normalizing fuel consumption value denoted Fuel under Predefined Conditions (FPC), which captures the effect of factors that are relevant but are not measured directly. The FPC, together with information available from truck sensors, is then compared against the actual fuel used on a given road segment, quantifying the effects associated with driver behavior or other variables of interest. We show that raw fuel consumption is a biased measure of driver performance, being heavily influenced by other factors such as high load or adversary weather conditions, and that using FPC leads to more accurate results. In this paper we also show evaluation the proposed method using large-scale, real-world, naturalistic database of heavy-duty vehicle operation

How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

Objective:This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background:Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method:Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results:The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion:Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application:Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving

Feasibility study of the electrification of the urban goods distribution transport system, part II

Based on the results from FFI project Feasibility study of the electrification of the urban goods distribution transport system (Vinnova reg. no. 2011-01803), this project aimed to investigate how urban goods distribution fleets can be electrified and how new logistics solutions and incentives can influence the transition in a positive way, considering the year 2015, 2020 and 2025.The project is divided into five work packages (WP). The purpose of the first work package, WP1 Fleet Electrification Study, was to evaluate at what rate it is possible for urban goods distribution fleets to become electrified. WP2, Service Impact Evaluation, is a description of ICT services to support electric trucks for goods distribution in cities. The purpose of WP3, Comparative Fleet Electrification Case and Best Practice Investigation, was to compare the electrification case for TGM/B\ue4ckebol in Gothenburg to a reference case in France and to the findings from research and demonstration projects within Europe. WP4, Method Description, package had two purposes. First, it aimed to broadly describe, from a project management perspective, how the project was executed. Second, it presents a review of the project based on the members’ views on how the project was conducted. Finally, WP5, Project Management, included the operative project management activities in the different work packages as well as administrative work such as financial reporting and communications on project progress and results.The project results show that it is difficult for the EVs to compete in 2015 considering a replacement of the diesel trucks with all-electric trucks. However, in 2020 the switch to an EV produces a small profit. This positive outcome for the EV is repeated in 2025 over 8 years of operation. Comparing the results, the two shifts solution never did get financially competitive with the diesel vehicle used in one shift. The reason for this was that the cost of unsocial hours was greater than the benefit of increased utilization of the EVs. Keeping the amount of unsocial hours down, while maximizing the utilization rate of the EV is therefore paramount. In other cities than Gothenburg, where congestion causes severe delays, the efficiency gained by distributing goods off-hours might balance the higher salary costs.The starting point in both the previous and in this project was the introduction of new technology to reduce the negative environmental impact of transports. As the project progressed it became clear that many parameters, other than strictly technological ones, influence the possibility to make a transition to electric distribution. One example, as described above, is the case of off-hour distribution, where the increased salary cost was too high to make the business case profitable. Another example is the limited range of electric vehicles. As the cost competitiveness of electric vehicles benefit greatly from specialization, the business relationship between the transport operator and the shippers becomes more important than in the case of a diesel vehicle. Long term assignments with well-defined transport routes are preferable to be able to use the vehicles long term and dare to take the higher investment cost

Splenic contraction and cardiovascular responses are augmented during apnea compared to rebreathing in humans

The spleen contracts during apnea, releasing stored erythrocytes, thereby increasing systemic hemoglobin concentration (Hb). We compared apnea and rebreathing periods, of equal sub-maximal duration (mean 137 s; SD 30), in eighteen subjects to evaluate whether respiratory arrest or hypoxic and hypercapnic chemoreceptor stimulation is the primary elicitor of splenic contraction and cardiovascular responses during apnea. Spleen volume, Hb, cardiovascular variables, arterial (SaO 2), cerebral (ScO 2), and deltoid muscle oxygen saturations (SmO 2) were recorded during the trials and end-tidal partial pressure of oxygen (P ETO 2) and carbon dioxide (P ETCO 2) were measured before and after maneuvers. The spleen volume was smaller after apnea, 213 (89) mL, than after rebreathing, 239 (95) mL, corresponding to relative reductions from control by 20.8 (17.8) % and 11.6 (8.0) %, respectively. The Hb increased 2.4 (2.0) % during apnea, while there was no significant change with rebreathing. The cardiovascular responses, including bradycardia, decrease in cardiac output, and increase in total peripheral resistance, were augmented during apnea compared to during rebreathing. The P ETO 2 was higher, and the P ETCO 2 was lower, after apnea compared to after rebreathing. The ScO 2 was maintained during maneuvers. The SaO 2 decreased 3.8 (3.1) % during apnea, and even more, 5.4 (4.4) %, during rebreathing, while the SmO 2 decreased less during rebreathing, 2.2 (2.8) %, than during apnea, 8.3 (6.2) %. We conclude that respiratory arrest per se is an important stimulus for splenic contraction and Hb increase during apnea, as well as an important initiating factor for the apnea-associated cardiovascular responses and their oxygen-conserving effects

Keeping pace with forestry : Multi-scale conservation in a changing production forest matrix

The multi-scale approach to conserving forest biodiversity has been used in Sweden since the 1980s, a period defined by increased reserve area and conservation actions within production forests. However, two thousand forest-associated species remain on Sweden's red-list, and Sweden's 2020 goals for sustainable forests are not being met. We argue that ongoing changes in the production forest matrix require more consideration, and that multi-scale conservation must be adapted to, and integrated with, production forest development. To make this case, we summarize trends in habitat provision by Sweden's protected and production forests, and the variety of ways silviculture can affect biodiversity. We discuss how different forestry trajectories affect the type and extent of conservation approaches needed to secure biodiversity, and suggest leverage points for aiding the adoption of diversified silviculture. Sweden's long-term experience with multi-scale conservation and intensive forestry provides insights for other countries trying to conserve species within production landscapes