Can low-cost multi-static LiDAR improve safety of connected autonomous vehicles?
Can low-cost multi-static LiDAR improve safety of connected autonomous vehicles?
This thesis is motivated by the following question: How can the performance of LiDAR sensors improve to reliably detect the surrounding environment at least as safely as with a competent human driver sitting in the driver’s seat? This research is much needed and timely.
LiDAR (Light Detection and Ranging) sensors have several applications. In this research the focus is on LiDAR application in connected autonomous vehicles CAV in order to improve their safety. CAV depend on their perception systems to gather information about their immediate surroundings. CAV need to detect their immediate environment including nearby vehicles, pedestrians, and other obstacles. LiDAR sensors complemented with cameras and other sensors using perception algorithms can improve safety by providing more accurate estimation of the surroundings and reducing the blind spots (Li and Ibanez-Guzman 2020).
This research investigates whether a single, rotating, and expensive LiDAR can be replaced by multiple, low-cost Solid-State LiDAR and advance road safety including the safety of CAV by helping reduce blind spots. In other terms if multi LiDAR sensors are better than one.
This research used both Physics and Engineering laboratories to test various scenarios of LiDAR sensors in order to draw conclusions and provide recommendations.
The aim of this research is to improve LiDAR sensors on CAV to better locate objects and ultimately pedestrians. Among other requirements for vehicles to become autonomous, LiDAR sensors need to function as well as the human eye, in order to eliminate pedestrian/vehicle collisions.
LiDAR sensors fall into several ranges and types. Each type has its own advantages and disadvantages, and the cost can differ. While some have argued that LiDAR sensors are expensive, one cannot put a cost on human life. Considering this, for the CAV industry to become more sustainable, there must be a cost-effective solution for installing LiDAR sensors onto vehicles.
This research project used both laboratory and field experiments using different LiDAR techniques and scenarios in order to answer the research question “Are Multistatic or Multiple LiDAR sensors better than a single LiDAR sensor in advancing road safety of CAV by seeing more (reducing the blind spot or dead zone)?.”
The conclusions drawn from this research suggest that two LiDAR sensors are better than one in reducing the blind spot, which can contribute in improving the safety of CAV. This was proven by understanding the theory of Two LiDAR setup in the Physics Lab pointing at one object using triangulation/trilateration method. The setup of two Lidar sensors combined with cameras was tested in the Engineering lab then using real life experiments pointing at a pedestrian. Using both Physics Lab and Engineering lab the two LiDAR setup proved to have fewer blind spots than one LiDAR setup.
LIDAR sensors, autonomous vehicles, road safety, smart cities, innovation, business model, state of the art, intelligent transport system, smart mobility, sustainability
University of Southampton
Nazer, Zeina
1f81566d-fd03-4203-a5f3-52180e539f63
2025
Nazer, Zeina
1f81566d-fd03-4203-a5f3-52180e539f63
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Nazer, Zeina
(2025)
Can low-cost multi-static LiDAR improve safety of connected autonomous vehicles?
University of Southampton, Doctoral Thesis, 124pp.
Record type:
Thesis
(Doctoral)
Abstract
This thesis is motivated by the following question: How can the performance of LiDAR sensors improve to reliably detect the surrounding environment at least as safely as with a competent human driver sitting in the driver’s seat? This research is much needed and timely.
LiDAR (Light Detection and Ranging) sensors have several applications. In this research the focus is on LiDAR application in connected autonomous vehicles CAV in order to improve their safety. CAV depend on their perception systems to gather information about their immediate surroundings. CAV need to detect their immediate environment including nearby vehicles, pedestrians, and other obstacles. LiDAR sensors complemented with cameras and other sensors using perception algorithms can improve safety by providing more accurate estimation of the surroundings and reducing the blind spots (Li and Ibanez-Guzman 2020).
This research investigates whether a single, rotating, and expensive LiDAR can be replaced by multiple, low-cost Solid-State LiDAR and advance road safety including the safety of CAV by helping reduce blind spots. In other terms if multi LiDAR sensors are better than one.
This research used both Physics and Engineering laboratories to test various scenarios of LiDAR sensors in order to draw conclusions and provide recommendations.
The aim of this research is to improve LiDAR sensors on CAV to better locate objects and ultimately pedestrians. Among other requirements for vehicles to become autonomous, LiDAR sensors need to function as well as the human eye, in order to eliminate pedestrian/vehicle collisions.
LiDAR sensors fall into several ranges and types. Each type has its own advantages and disadvantages, and the cost can differ. While some have argued that LiDAR sensors are expensive, one cannot put a cost on human life. Considering this, for the CAV industry to become more sustainable, there must be a cost-effective solution for installing LiDAR sensors onto vehicles.
This research project used both laboratory and field experiments using different LiDAR techniques and scenarios in order to answer the research question “Are Multistatic or Multiple LiDAR sensors better than a single LiDAR sensor in advancing road safety of CAV by seeing more (reducing the blind spot or dead zone)?.”
The conclusions drawn from this research suggest that two LiDAR sensors are better than one in reducing the blind spot, which can contribute in improving the safety of CAV. This was proven by understanding the theory of Two LiDAR setup in the Physics Lab pointing at one object using triangulation/trilateration method. The setup of two Lidar sensors combined with cameras was tested in the Engineering lab then using real life experiments pointing at a pedestrian. Using both Physics Lab and Engineering lab the two LiDAR setup proved to have fewer blind spots than one LiDAR setup.
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Published date: 2025
Additional Information:
Zeina is Co-Founder of Cities Forum, External Expert for UK Department for Transport and Chair of ITS-UK Road User Charging Forum.
Zeina has over 25 years of global experience in strategy, consulting and innovation at KPMG, Parsons, Jacobs and AECOM in the Middle East, Europe, S.E. Asia and USA.
A leading advisor for governments around the world in Smart & Sustainable Cities, Intelligent Mobility, Road User Charging, Connected Autonomous Vehicles, and Road Safety standards.
Zeina specializes in Entrepreneurship, Innovation, Growth Strategy and Policy.
After 7 years working in the USA, Zeina moved to London in 2005 to support international projects including leading international consultancy work with Arcadis for M25 Integral Demand Management. Zeina also provided support to financial design of the New Zealand Toll project with Deloitte, supported Strategy&PWC in the Project Management Office (PMO) for Abu Dhabi Mobility and supported Dubai RTA in shaping the future of transport in public transport and autonomous vehicles.
Zeina led Research project for CCAV at Department for Transport on assessing safety of Teleoperation on UK public roads.
Zeina is Jury Member for EISMEA established by European Commission, Jury member of GLOMO Award in innovation at MWC, Jury member of CiTTi Awards in UK.
Zeina was chair of the ISO Technical Working Group TC204 and served as Director on the Board of Women in Transport International.
Zeina is a member of Close the Data Gap at University of Southampton for creating an equitable future in transport data and committee member of Equality Diversity Inclusion committee.
Zeina holds BSc. of Civil Engineering from American University of Beirut, MSc. in Transport Engineering from University of Texas at Austin, Master of Business Administration from University of Chicago and currently pursuing her Postgraduate Studies in Smart Cites at University of Southampton.
Zeina is registered Professional Engineer in USA. A keynote speaker & author of over 100 papers of magazines and conferences around the world.
Zeina is British & Lebanese and is fluent in Arabic, French & English.
Keywords:
LIDAR sensors, autonomous vehicles, road safety, smart cities, innovation, business model, state of the art, intelligent transport system, smart mobility, sustainability
Identifiers
Local EPrints ID: 501916
URI: http://eprints.soton.ac.uk/id/eprint/501916
PURE UUID: 829da2d7-3bfd-4aff-802d-f632a76c5091
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Date deposited: 12 Jun 2025 16:31
Last modified: 11 Sep 2025 03:05
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