Role of infrared sensing in autonomous driving (Conference Presentation)

2019 ◽  
Author(s):  
Jeffrey Coote
Keyword(s):  
Autonomous Driving ◽  
Infrared Sensing

scholarly journals The Role of Maps in Autonomous Driving Simulations

2020 ◽  
Vol 48 (4) ◽  
pp. 363-368
Author(s):  
Árpád Barsi ◽  
András Csepinszky ◽  
János Máté Lógó ◽  
Nikol Krausz ◽  
Vivien Potó
Keyword(s):  
Autonomous Driving ◽  
Information Storage ◽  
The Road ◽  
Data Formats ◽  
Data Capturing ◽  
Physical Information ◽  
Computer Based ◽  
Quality Map ◽  
Mapping Systems

The vehicles of the conditional, high, and full automation levels have a common unique sensor, the map. The term map has undergone a significant change because the spatial resolution has been increased considerably, the road infrastructure and its neighborhood are represented with higher accuracy in 3D. The development of these vehicles requires enormous efforts, where computer-based techniques, like the simulations, can offer a helping hand. The autonomous simulations will be supported by high-quality map information, which generates interest in the best field data-capturing techniques. The paper provides an overview of the available modern surveying methodologies, then introduces the most preferred data formats – both in physical information storage and in exchanging information content between mapping systems. Some examples are presented to demonstrate the usage of the relevant map-making outputs in automotive simulators.

scholarly journals Monitoring Mobile Information Processing Systems

2020 ◽  
Vol 23 (4) ◽  
pp. 835-847
Author(s):  
Oleg Panarin ◽  
Igor Zacharov
Keyword(s):  
Monitoring System ◽  
Time Series Data ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Series Data ◽  
Continuous Assessment ◽  
Fitness To Drive ◽  
The Core ◽  
It Systems

We describe the implementation of the monitoring for the IT systems at the core of the autonomous driving vehicle. The role of the monitoring is to assist in decision to start the driving cycle and continuous assessment for the fitness to drive the vehicle. The requirements for the monitoring system with the increased resiliency and data replication make it sufficiently different from standard monitoring systems and warrant a unique implementation tuned for the autonomous driving requirements. The monitoring system combines the OS events and real-time measurements of sensor data. The information is stored in flat files for emergency access as well as in a Time Series Data Base (TSDB).

scholarly journals In Cars (Are We Really Safest of All?): Interior Sensing and Emotional Opacity

2021 ◽  
Author(s):  
Andrew McStay ◽  
Lachlan Urquhart
Keyword(s):  
Autonomous Driving ◽  
Monitoring Systems ◽  
Affective States ◽  
The Road ◽  
Social Trends ◽  
Driver Monitoring ◽  
European Regulations ◽  
On The Road ◽  
Vision Zero

This paper considers car driver monitoring systems that measure bodies to infer and react to emotions and other affective states. Situated within social trends in personalisation and automation, developers of driver monitoring systems promise increased safety on the road and comfort for cabin occupants. The impetus is threefold, namely: (1) European road safety policy seeks to vastly reduce road deaths using computational surveillance; (2) interest in the role of safety solutions based on in-cabin sensing of emotion and affective states of drivers and passengers; and 3) autonomous driving trends changing the nature of interactions between vehicle and driver. These systems are of special interest because they are backed by policy and standards initiatives, not least the European Union’s Vision Zero policy that seeks to reduce road death to zero, and industry-oriented safety programmes like the New Car Assessment Programme (NCAP). Informed by 13 expert interviews with interviewees working in and around in-cabin sensing developments, this paper identifies and explores features of emergent in-cabin profiling through emotional AI and biometric measures. It then carries ambivalent insights found into analysis of applicable European regulations, also finding a deep ambivalence in the politics of Emotional AI for interior sensing of cabins and occupants.

scholarly journals DIGITAL TRANSFORMATION OF CAR DEALERSHIP BUSINESS IN CONDITIONS OF THE CRISIS CAUSED BY COVID-19

2021 ◽  
Vol 49 (2) ◽  
pp. 198-214
Author(s):  
Cherviakova V ◽  
Keyword(s):  
Consumer Preferences ◽  
Autonomous Driving ◽  
Digital Transformation ◽  
Strategy Formation ◽  
Automotive Market ◽  
Sales Channels ◽  
Online Sales ◽  
Retail Strategy ◽  
Object Of Study

The article is devoted to the study of the process of formation of the car retail strategy in the conditions of digital transformation and challenges caused by COVID-19. Object of study - digital transformation of car dealership business in conditions of COVID-19. Purpose - to investigate the process of car retail strategy formation in conditions of digital transformation and challenges caused by COVID-19. Methods of research - analysis, synthesis, generalization, systematization, graphic. The article examines how the change in consumer preferences, digitalization, and ACES trends (autonomous driving, connectivity, the electrification of vehicles, and shared mobility) cause the transformation of car retail. The current situation with COVID-19 is accelerating the introduction of digital car purchase models and the development of online sales channels. The essence of modern trends of the car retail market and their consequences for car manufacturers, dealers, and clients have been analyzed. Five archetypes of future retail strategies in the automotive market have been identified, with their characteristics, prerequisites, and implications for automakers, dealers, and customers. The state and prospects of the automotive market of Ukraine have been investigated. It was concluded that the role of official dealerships in vehicle trade, maintenance, and repair will gradually decrease. A number of measures were proposed to ensure the survival of dealerships in Ukraine. KEY WORDS: DIGITAL TRANSFORMATION, DIGITALIZATION. CAR DEALERSHIP BUSINESS, RETAIL STRATEGY, BUSINESS MODEL, COVID-19.

scholarly journals On infusing reachability-based safety assurance within planning frameworks for human–robot vehicle interactions

2020 ◽  
Vol 39 (10-11) ◽  
pp. 1326-1345 ◽  
Author(s):  
Karen Leung ◽  
Edward Schmerling ◽  
Mengxuan Zhang ◽  
Mo Chen ◽  
John Talbot ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Other ◽  
Proactive Behavior ◽  
The Road ◽  
Planning Algorithm ◽  
Intent Prediction ◽  
On The Road ◽  
Free Interaction

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road: a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This article introduces a minimally interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart while respecting static obstacles such as a road boundary wall. We leverage reachability analysis to construct a real-time (100 Hz) controller that serves the dual role of (i) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (ii) assuring safety by maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic-weaving experiments wherein two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner’s expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety.

The Effects of Interruption on Takeover Performance in Automated Vehicles

2021 ◽  
Vol 65 (1) ◽  
pp. 1538-1540
Author(s):  
Yongdeok Yun ◽  
Rohae Myung
Keyword(s):  
Driving Simulator ◽  
Autonomous Driving ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Baseline Monitoring ◽  
Human Driver ◽  
Significant Difference ◽  
Short Video

As autonomous driving technology developing, the role of human driver becomes a passive passenger in an automated vehicle. Drivers would perform non-driving related tasks instead of driving, especially multitasking.. However, most of studies did not considered multitasking as NDRTs. In this study, experiment considering self-interruption is conducted and investigate effects of interruption on takeover performance. To investigate effects of interruption on takeover performance, experiment using driving simulator was conducted. Watching a short video was selected as a NDRT and there were three conditions of NDRT according to self-interruption: ‘Baseline’, ‘Monitoring condition’, and ‘Smartphone condition’. Takeover performance was measured by eyes-on time and deactivation time. There was no statistically significant difference for eyes-on time depending on interruption. However, interruption has a significant effect on deactivation time. Also, it was more effective to use a smartphone during interruption.

scholarly journals The Role of NVH in Autonomous Driving

2016 ◽  
Vol 21 (S1) ◽  
pp. 54-54 ◽  
Author(s):  
Davide Caprioli
Keyword(s):  
Autonomous Driving

scholarly journals Role of HD Maps in Unmanned Vehicles

2020 ◽  
pp. 109-117
Author(s):  
Alexander Gaiosh
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unmanned Vehicles

This article discusses the possibilities of applying HD-mapping solutions in autonomous driving. A description and comparison of the OpenDRIVE and Lanelet formats are provided. There are also descriptions of HD-maps usage in systems of perception, localization, and planning of the movement of an autonomous vehicle, as well as the concept of a digital road model is given.

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