Abstract

International audience; This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows:- Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology.- Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure.- Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles.- Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance.- Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment.- Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed.- Sections 62.7 and 62.8 conclude the chapter with a discussion of future prospects, and provide references to further reading and additional resources.

Abstract

Merging is one of the most critical scenarios that can be found in road transport. In this maneuver, the driver is subjected to a high mental load due to the large amount of information he handles, while making decisions becomes a crucial issue for their safety and those in adjacent vehicles. In previous works, it was studied how the merging maneuver affected the cognitive load required for driving by means of an eye tracking system, justifying the proposal of a driver assistance system for the merging maneuver on highways. This paper presents a merging assistance system based on communications between vehicles, which allows vehicles to share internal variables of position and speed and is implemented on a mobile device located inside the vehicle. The system algorithm decides where and when the vehicle can start the merging maneuver in safe conditions and provides the appropriate information to the driver. Parameters and driving simulator tests are used for the interface definition to develop the less intrusive and demanding one. Afterward, the system prototype was installed in a real passenger car and tests in real scenarios were conducted with several drivers to assess usability and mental load. Comparisons among alternative solutions are shown and effectiveness is assessed. Document type: Article

Abstract

utomated truck platooning is getting an increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency, and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have been inattentive during highly automated driving. In this study, we investigated truck drivers’ take-over response times after a system initiated request to take back control in non-critical truck platooning scenarios. 22 professional truck drivers participated in the truck driving simulator experiment and everyone was instructed to drive under three task conditions during highly automated driving: Driver monitoring condition (drivers were instructed to monitor the surroundings), Driver not-monitoring condition (drivers were provided with a hand-held tablet and were asked to use this), and Eyes-closed condition (drivers were not allowed to open their eyes). The total take-over response time was divided into the perception response time and the movement response time by manual video annotation. Results showed significantly longer total take-over times with high variability in both Driver not-monitoring and Eyes-closed conditions compared to the Driver monitoring condition. Hand movement response time was found to be the dominant component of the total take-over time, being influenced by the motoric manoeuvres to resume physical readiness before taking over control (e.g., putting away the hand-held tablet, or adjusting seating position). These results suggest the importance of a personalized driver readiness predictor as an input parameter for a safe and comfortable transition of control. © 2019 Elsevier Ltd