Location solutions for Intelligent Transport Systems (ITS)

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Presented by Nigel Wall, Director, Shadow Creek Consultancy Ltd in the techUK Satellite Applications & Services Conference, 2nd Oct. 2015

Technology

11
Location solutions for
Cooperative Transport Systems
Nigel Wall
Director, Shadow Creek Consulting Ltd
Chair of ITS UK Communications Interest
Group
Chair of RIN, Land Navigation &
Location Group
Director, Climate Associates Ltd

2
Agenda
• Cooperative Transport Applications
• Location accuracy required.
• Location context - mapping
• GNSS – essential but not sufficient
• Future work:
• Vulnerable users

3
Cooperative Active Safety
Avoidance, Preventive, Active Safety Passive Safety
Crash
Intervention
Active vehicle
control
Safety
systems
Information
Foresighted
driving
Crash
Support
Warning & assistance
systems
Rescue
systems
& services
Incident range:
Time: hours minutes 60s 10s 1s 50mS - minutes - hours
Distance: 100km 10km 1km 100m 10m 1m -50cm
performance needed:
Updates: 1min 10s 1s 100ms 100ms
Location 10m 10m 1m 10cm 10cm 3m
Accuracy
Adapted from
time
Crashprobability
Absolute Position Relative Position

4
Application Location Accuracy?
incident detection and warning 10m
warning of reduced visibility 100m
safety distance and speed advice 1m
traffic merging 1m
road surface warning and defensive driving 5m
wrong way driver detection 2m
obstacle detection and frontal collision prevention 50cm
rear end collision prevention 5m
safe overtaking & lane change assistance 50cm
lane / road departure prevention 50cm
dangerous curve warning 50m
vulnerable road user detection 50cm + velocity
clear lane for emergency vehicles 50cm
intersection collision prevention (including traffic signals) 2m
Cooperative ITS Applications

5
Side
Crash
Lane Change
Assistance
Safe Speed
& Safe Distance
(Radar based)
Lane
Support
Rear
Detection
Blind Spot
Collision
Mitigation
Autonomous Intelligent Vehicle
Adapted from © SAFESPOT Integrated Project

6
V2V & V2I
Rear
Detection
Blind Spot Side
Crash
Lane Change
Assistance
Lane
Support
Collision
Mitigation
Extended Rear
Detection &
Blind Spot
Cooperative
Warning
Infrastructure Based
Warning
Road Side Equipment
(local or remote)
Intelligent Cooperative System
Extended Safe Distance
and Speed
Adapted from © SAFESPOT Integrated Project

7
LOCAL DYNAMIC MAP
Map from
provider
Landmarks for
referencing
Tall Tree
Output of cooperative
sensing/processing
Temporary
regional info
!
Accident (just
occurred)
Fog bank
Slippery road
surface (ice)
Own Vehicle – position,
direction, speed, status, etc
Signaling
phases
Vehicles
in queue
Aim: to represent the
vehicle’s surroundings with
all static and dynamic
safety-relevant elements
Legacy vehicle
Adapted from © SAFESPOT Integrated Project

8
Integration of multiple sensors.
Camera derived information
Radar – Advanced Cruise Control
Pictures from Google Images: owners are acknowledged and thanked

9
• Vehicle based systems:
• Location information
• Context (map)
• Velocity & accelerate & course along the road space
• Information of trajectory transmitted to other road users
• Infrastructure information:
• Presence of Pavement / sidewalk
• Pedestrian crossings
• Vulnerable road users:
• Pedestrians, Prams, Cycles & Horse riders
• Deaf pedestrian, slow movers, and children
• Equipped with GNSS smartphone + app, or dedicated device
• Identify when on road surface and at risk
• Able to alert user and transmit to vehicles, when appropriate
Vulnerable Road Users

1010
Questions?
www.its-uk.org.uk
www.climate-associates.com www.shadow-creek.biz
For more information see:
www.rin.org.uk www.transport-associates.net

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Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers: * What is Vector Search? * Importance and benefits of vector search * Practical use cases across various industries * Step-by-step implementation guide * Live demos with code snippets * Enhancing LLM capabilities with vector search * Best practices and optimization strategies Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications. #MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology

Building Production Ready Search Pipelines with Spark and Milvus

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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU

panagenda

Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/ DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen! Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell. Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten. Diese Themen werden behandelt - Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten - Wie funktionieren CCB- und CCX-Lizenzen wirklich? - Verstehen des DLAU-Tools und wie man es am besten nutzt - Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw. - Praxisbeispiele und Best Practices zum sofortigen Umsetzen

Removing Uninteresting Bytes in Software Fuzzing

Aftab Hussain

Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process. In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds. - These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

Full-RAG: A modern architecture for hyper-personalization

Zilliz

Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.

Climate Impact of Software Testing at Nordic Testing Days

Kari Kakkonen

My slides at Nordic Testing Days 6.6.2024 Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.

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Location solutions for Intelligent Transport Systems (ITS)

1. 11 Location solutions for Cooperative Transport Systems Nigel Wall Director, Shadow Creek Consulting Ltd Chair of ITS UK Communications Interest Group Chair of RIN, Land Navigation & Location Group Director, Climate Associates Ltd

2. 2 Agenda • Cooperative Transport Applications • Location accuracy required. • Location context - mapping • GNSS – essential but not sufficient • Future work: • Vulnerable users

3. 3 Cooperative Active Safety Avoidance, Preventive, Active Safety Passive Safety Crash Intervention Active vehicle control Safety systems Information Foresighted driving Crash Support Warning & assistance systems Rescue systems & services Incident range: Time: hours minutes 60s 10s 1s 50mS - minutes - hours Distance: 100km 10km 1km 100m 10m 1m -50cm performance needed: Updates: 1min 10s 1s 100ms 100ms Location 10m 10m 1m 10cm 10cm 3m Accuracy Adapted from time Crashprobability Absolute Position Relative Position

4. 4 Application Location Accuracy? incident detection and warning 10m warning of reduced visibility 100m safety distance and speed advice 1m traffic merging 1m road surface warning and defensive driving 5m wrong way driver detection 2m obstacle detection and frontal collision prevention 50cm rear end collision prevention 5m safe overtaking & lane change assistance 50cm lane / road departure prevention 50cm dangerous curve warning 50m vulnerable road user detection 50cm + velocity clear lane for emergency vehicles 50cm intersection collision prevention (including traffic signals) 2m Cooperative ITS Applications

5. 5 Side Crash Lane Change Assistance Safe Speed & Safe Distance (Radar based) Lane Support Rear Detection Blind Spot Collision Mitigation Autonomous Intelligent Vehicle Adapted from © SAFESPOT Integrated Project

6. 6 V2V & V2I Rear Detection Blind Spot Side Crash Lane Change Assistance Lane Support Collision Mitigation Extended Rear Detection & Blind Spot Cooperative Warning Infrastructure Based Warning Road Side Equipment (local or remote) Intelligent Cooperative System Extended Safe Distance and Speed Adapted from © SAFESPOT Integrated Project

7. 7 LOCAL DYNAMIC MAP Map from provider Landmarks for referencing Tall Tree Output of cooperative sensing/processing Temporary regional info ! Accident (just occurred) Fog bank Slippery road surface (ice) Own Vehicle – position, direction, speed, status, etc Signaling phases Vehicles in queue Aim: to represent the vehicle’s surroundings with all static and dynamic safety-relevant elements Legacy vehicle Adapted from © SAFESPOT Integrated Project

8. 8 Integration of multiple sensors. Camera derived information Radar – Advanced Cruise Control Pictures from Google Images: owners are acknowledged and thanked

9. 9 • Vehicle based systems: • Location information • Context (map) • Velocity & accelerate & course along the road space • Information of trajectory transmitted to other road users • Infrastructure information: • Presence of Pavement / sidewalk • Pedestrian crossings • Vulnerable road users: • Pedestrians, Prams, Cycles & Horse riders • Deaf pedestrian, slow movers, and children • Equipped with GNSS smartphone + app, or dedicated device • Identify when on road surface and at risk • Able to alert user and transmit to vehicles, when appropriate Vulnerable Road Users

10. 1010 Questions? www.its-uk.org.uk www.climate-associates.com www.shadow-creek.biz For more information see: www.rin.org.uk www.transport-associates.net

Location solutions for Intelligent Transport Systems (ITS)

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