The effects of alcohol on driver performance in a decision making situation

The results are reviewed of driving simulator and in-vehicle field test experiments of alcohol effects on driver risk taking. The objective was to investigate changes in risk taking under alcoholic intoxication and relate these changes to effects on traffic safety. The experiments involved complex 15 minute driving scenarios requiring decision making and steering and speed control throughout a series of typical driving situations. Monetary rewards and penalties were employed to simulate the real-world motivations inherent in driving. A full placebo experimental design was employed, and measures related to traffic safety, driver/vehicle performance and driver behavior were obtained. Alcohol impairment was found to increase the rate of accidents and speeding tickets. Behavioral measures showed these traffic safety effects to be due to impaired psychomotor performance and perceptual distortions. Subjective estimates of risk failed to show any change in the driver's willingness to take risks when intoxicated

Real scenario and simulations on GLOSA traffic light system for reduced CO2 emissions, waiting time and travel time

Cooperative ITS is enabling vehicles to communicate with the infrastructure to provide improvements in traffic control. A promising approach consists in anticipating the road profile and the upcoming dynamic events like traffic lights. This topic has been addressed in the French public project Co-Drive through functions developed by Valeo named Green Light Optimal Speed Advisor (GLOSA). The system advises the optimal speed to pass the next traffic light without stopping. This paper presents results of its performance in different scenarios through simulations and real driving measurements. A scaling is done in an urban area, with different penetration rates in vehicle and infrastructure equipment for vehicular communication. Our simulation results indicate that GLOSA can reduce CO2 emissions, waiting time and travel time, both in experimental conditions and in real traffic conditions.Comment: in 22nd ITS World Congress, Oct 2015, Bordeaux, France. 201

In the Truman show: generating dynamic scenarios in a driving simulator

All the devices, animals, and people make their decisions based on what you're doing, but you don't know it or even notice it. Your world is that of Truman Burbank, from the 1998 movie The Truman Show. With this idea in mind, we've taken the movie metaphor to implement a prototype simulation system where the user steps into Truman's shoes. The set of our "movie" is a driving simulator, and the user is learning to drive a car. During the driving lessons, users drive in a virtual world that lets them experience all kinds of traffic scenarios. The system generates the scenarios with the student as the focal point, and the other traffic entities respond to the student's behavior, without the student noticing. To control the traffic scenarios and make them more effective, our prototype employs an agent-based framework. In this framework, each entity in the simulator is an actor agent playing a role. The prototype also includes a hierarchy of directors that directs the main action and the behind-the-scenes activity. The advantage of the movie metaphor is that it helps separate scenario description from scenario playing. The agents can read their required information from a script and perform their actions based on that information. Using this framework lets us build software that's extensible, maintainable, and easy to understan

What Am I Testing and Where? Comparing Testing Procedures based on Lightweight Requirements Annotations

[Context] The testing of software-intensive systems is performed in different test stages each having a large number of test cases. These test cases are commonly derived from requirements. Each test stages exhibits specific demands and constraints with respect to their degree of detail and what can be tested. Therefore, specific test suites are defined for each test stage. In this paper, the focus is on the domain of embedded systems, where, among others, typical test stages are Software- and Hardware-in-the-loop. [Objective] Monitoring and controlling which requirements are verified in which detail and in which test stage is a challenge for engineers. However, this information is necessary to assure a certain test coverage, to minimize redundant testing procedures, and to avoid inconsistencies between test stages. In addition, engineers are reluctant to state their requirements in terms of structured languages or models that would facilitate the relation of requirements to test executions. [Method] With our approach, we close the gap between requirements specifications and test executions. Previously, we have proposed a lightweight markup language for requirements which provides a set of annotations that can be applied to natural language requirements. The annotations are mapped to events and signals in test executions. As a result, meaningful insights from a set of test executions can be directly related to artifacts in the requirements specification. In this paper, we use the markup language to compare different test stages with one another. [Results] We annotate 443 natural language requirements of a driver assistance system with the means of our lightweight markup language. The annotations are then linked to 1300 test executions from a simulation environment and 53 test executions from test drives with human drivers. Based on the annotations, we are able to analyze how similar the test stages are and how well test stages and test cases are aligned with the requirements. Further, we highlight the general applicability of our approach through this extensive experimental evaluation. [Conclusion] With our approach, the results of several test levels are linked to the requirements and enable the evaluation of complex test executions. By this means, practitioners can easily evaluate how well a systems performs with regards to its specification and, additionally, can reason about the expressiveness of the applied test stage.TU Berlin, Open-Access-Mittel - 202

Long-lasting virtual motorcycle-riding trainer effectiveness

This work aimed to test the long-lasting effects of learning acquired with a virtual motorcycle-riding trainer as a tool to improve hazard perception. During the simulation, the rider can interact with other road actors and experience the most common potential accident situations in order to learn to modify his or her behavior to anticipate hazards and avoid crashes. We compared performance to the riding simulator of the two groups of participants: the experimental group, which was trained with the same simulator one year prior, and the control group that had not received any type of training with a riding or driving simulator. All of the participants had ridden a moped in the previous 12 months. The experimental group showed greater abilities to avoid accidents and recognize hazards in comparison to their performance observed a year before, whereas the performance of the control group was similar to that of the experimental group 1 year before in the first two sessions, and even better in the third. We interpreted this latter result as a consequence of their prior on-road experience. Also, the fact that the performance of the experimental group at the beginning of the follow-up is better than that recorded at the end of the training 1 year before is in line with the idea of a transfer from the on-road experience to the simulator. The present data confirm our main expectation that the effectiveness of the riding training simulator on the ability to cope with potentially dangerous situations persists over time and provides additional evidence in favor of the idea that simulators may be considered useful tools for training the ability to detect and react to hazards, leading to an improvement of this higher-order cognitive skill that persists over time. Implications for the reciprocal influence of the training with the simulator and the on-the road experience are discussed as well

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.