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Localization Requirements for Autonomous Vehicles

Authors:
  • Xona Space Systems

Abstract and Figures

Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10^-8 probability of failure per hour of operation. Longitudinal, lateral, and vertical localization error bounds (alert limits) and 95% accuracy requirements are derived based on US road geometry standards (lane width, curvature, and vertical clearance) and allowable vehicle dimensions, where the aim is knowledge that the vehicle is within the lane and what road level it is on. For passenger vehicles operating on freeway roads, the result is a required lateral error bound of 0.57 m (0.20 m, 95%), a longitudinal bound of 1.40 m (0.48 m, 95%), a vertical bound of 1.30 m (0.43 m, 95%), and an attitude bound in each direction of 1.50 deg (0.51 deg, 95%). On local streets, the road geometry makes requirements more stringent where lateral and longitudinal error bounds of 0.29 m (0.10 m, 95%) are needed with an orientation requirement of 0.50 deg (0.17 deg, 95%).
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Localization
Requirements
for Autonomous
Vehicles
Ty ler G. R. Rei d, Sarah E. Houts*,
Robert Cammarata*, Graham Mills*,
Siddharth Agarwal*, Ankit Vora*, &
Gaurav Pandey
Research & Advanced Engineering, Ford Motor Company
*Ford Autonomous Vehicles, LLC
Email: treid21@ford.com
Outline
1
Intro to
Localization
Localization
Geometry
Localization
Integrity
AV Requirements
& Comparison
Outline
2
Intro to
Localization
Localization
Geometry
Localization
Integrity
AV Requirements
& Comparison
Introduction
3
where am I? where am I in context?
Localization Mapping
GPS
1996-Present
3 m
Transit
1964-1996
25 m
Celestial/Chrono
1770-1920
3000 m
Loran
1940s-2010
460 m
A Brief History of Navigation
4
‘Moore’s Law’ of Navigation
5
Trend: 10x
increase in
accuracy
every ~30
years
2020s:
Decade of
the
decimeter
Not good enough for AV’s
6
AV Localization Sensors
7
Traffic / Roads
Weather
Lidar
Vision
GNSS
Other
Multimodal Sensing
8
Cameras
Lidar
Radar
GPS / IMU
Localization Requirements
9
How well do we
need to know our
position to ensure
we are within the
lane?
Localization Requirements
10
Geometry Target L e v el of
Safety
Outline
11
Intro to
Localization
Localization
Geometry
Localization
Integrity
AV Requirements
& Comparison
Road Geometry
12
As turns become tighter, lateral and longitudinal requirements
become more coupled. Coupling is stronger on local streets vs. freeways.
US Road Geometry
13
Road Type Design Speed
[km/h]
Lane Width
[m]
Minimum
Radius** [m]
Freeway 80 130 3.6 195**
Interchanges 30 110 3.6 5.4 15 150
Arterial 50 100 3.3 3.6 70**
Collector 50 3.0 3.6 70**
Local 30-50 2.7*3.6 10**
Hairpin / Cul
-de
-
Sac < 20 6.0 7
Single Lane
Roundabout < 20 4.3 11
*The lower bound of 2.7 m is the exception, not the rule, and is typically reserved for
residential streets with low traffic volumes.
**Based on design speeds and limiting values of rate of roadway superelevation eand
coefficient of friction f.
Vehicle Dimensions
14
Vehicle Type Width
[m]
Length
[m]
Height
[m]
Passenger (P) 2.1 5.8 1.3
Single Unit Truck
(SU) 2.4 9.2 3.4 4.1
City Bus 2.6 12.2 3.2
Semitrailer 2.4 2.6
13.9
22.4
4.1
Based on data from: American Association of State Highway and Transportation Officials, “A
Policy on Geometric Design of Highways and Streets.” 2001.
Bounding Box Design
15
16
*Based on US passenger vehicle dimension limits
Lateral / Longitudinal Maximum Allowable Error Tradeoff
Highways
17
Lateral / Longitudinal Maximum Allowable Error Tradeoff
Local Streets
*Based on US passenger vehicle dimension limits
Vertical Requirements
We need to determine what
road level we are on.
US roads specify a minimum
clearance height of 4.4 m.
We need to know our vertical
position to a fraction of this
clearance height to resolve our
road level.
18
min.vertical clearance
3=
4.4 m
3= 1.47 m
‘High Five’ Interchange in Dallas, TX
(US 75 & I-635)
Bounding Box
19
Orientation Requirements
Errors in orientation inflate the
position protection level around
the vehicle.
We must specify limits on
acceptable attitude errors as well
as position errors.
This becomes a trade in
allocating allowable position and
attitude errors.
20
Outline
21
Intro to
Localization
Localization
Geometry
Localization
Integrity
AV Requirements
& Comparison
Integrity
22
Probability of
Failure per Hour
General
Programmable
Electronics
(IEC-61508)
Automotive
(ISO 26262)
Aviation
(DO
-
178/254)
Railway
(CENELEC
50126/128/129)
n/a (SIL-0) QM DAL-E (SIL-0)
10-6 10-5 SIL-1 ASIL-A DAL-D SIL-1
10-7 10-6 SIL-2 ASIL-B/C DAL-C SIL-2
10-8 10-7 SIL-3 ASIL-D DAL-B SIL-3
10-9 10-8 SIL-4 - DAL-A SIL-4
What Safety Integrity Level do we require for localization?
Approximate Cross-Domain Mapping of Safety Integrity Levels
Tar ge t L eve l of S afe ty
23
Yea r
1 fatality / 108miles
0.02 fatalities / 108miles
Tar ge t L eve l of S afe ty
24
Target Level
of Safety
2x10-10
fatal accidents /
mile
Tar ge t L eve l of S afe ty
25
Fatal
Accident to
Incident
Ratio
Target Level
of Safety
2x10-10
fatal accidents /
mile
Tar ge t L eve l of S afe ty
26
Yea r
1 Fatal Accident /
172 Total Accidents
1 Fatal Accident /
14 Total Accidents
Tar ge t L eve l of S afe ty
27
Fatal
Accident to
Incident
Ratio
Target Level
of Safety
2x10-10
fatal accidents /
mile
10-2
fatal accidents /
failure
Tar ge t L eve l of S afe ty
28
Fatal
Accident to
Incident
Ratio
Virtual Driver System
Target Level
of Safety
Vehicle
Systems
Localization
Perception Control
Planning Active
Chassis
Actuation
2x10-10
fatal accidents /
mile
10-2
fatal accidents /
failure
2x10-8
failures /
mile
Tar ge t L eve l of S afe ty
29
Fatal
Accident to
Incident
Ratio
Virtual Driver System
Target Level
of Safety
Vehicle
Systems
Localization
Perception Control
Planning Active
Chassis
Actuation
2x10-10
fatal accidents /
mile
10-2
fatal accidents /
failure
10-8
failures / mile
10-8
failures / mile
10-9
failures / mile
2x10-8
failures /
mile
Integrity
30
Probability of
Failure per Hour
General
Programmable
Electronics
(IEC-61508)
Automotive
(ISO 26262)
Aviation
(DO
-
178/254)
Railway
(CENELEC
50126/128/129)
n/a (SIL-0) QM DAL-E (SIL-0)
10-6 10-5 SIL-1 ASIL-A DAL-D SIL-1
10-7 10-6 SIL-2 ASIL-B/C DAL-C SIL-2
10-8 10-7 SIL-3 ASIL-D DAL-B SIL-3
10-9 10-8 SIL-4 - DAL-A SIL-4
Integrity
31
Outline
32
Intro to
Localization
Localization
Geometry
Localization
Integrity
AV Requirements
& Comparison
33
Requirements for US Highways
34
Vehicle
Type
Accuracy (95%) Alert Limits Prob. of
Failure
(Integrity)
Lat.
[m]
Long.
[m]
Vert.
[m]
Att*
[deg]
Lat.
[m]
Long.
[m]
Vert.
[m]
Att*
[deg]
Mid-Size 0.24
0.48
0.44 0.51 0.72 1.40 1.30 1.5 10-9 /mile
(10-8 /h)
Full-Size 0.23
0.48
0.44 0.51 0.66 1.40 1.30 1.5 10-9 /mile
(10-8 /h)
Pickup 0.21
0.48
0.44 0.51 0.62 1.40 1.30 1.5 10-9 /mile
(10-8 /h)
Vehicle
Limits
0.20
0.48
0.44 0.51 0.57 1.40 1.30 1.5 10-9 /mile
(10-8 /h)
*Error in each direction (roll, pitch, and heading).
Example of Desired Lateral Error Distribution
35
±1.96σ
(95%)
±0.20 m
±5.73σ
(99.999999% or Probability of 1 10-8 )
±0.57 m
μ= 0
36
Requirements for Local Roads
37
Vehicle
Type
Accuracy (95%) Alert Limits Prob. of
Failure
(Integrity)
Lat.
[m]
Long.
[m]
Vert.
[m]
Att*
[deg]
Lat.
[m]
Long.
[m]
Vert.
[m]
Att*
[deg]
Mid-Size 0.15 0.15 0.48 0.17 0.44 0.44 1.40 0.5 10-9 /mile
(10-8 /h)
Full-Size 0.13 0.13 0.48 0.17 0.38 0.38 1.40 0.5 10-9 /mile
(10-8 /h)
Standard
Pickup 0.12 0.12 0.48 0.17 0.34 0.34 1.40 0.5 10-9 /mile
(10-8 /h)
Passenger
Vehicle
Limits
0.10 0.10 0.48 0.17 0.29 0.29 1.40 0.5 10-9 /mile
(10-8 /h)
*Error in each direction (roll, pitch, and heading).
Comparison to Other Modes of Transport
38
Comparison to Other Modes of Transport
39
Tra ns po rt at ion
Mode Operation
Accuracy
(95%)
[m]
Alert
Limit
[m]
Probability of
Failure
Maritime
Ocean / Coastal 10 25** 10-5 / 3 hours
Port / Hydrography /
Drilling 12.5** 10-5 / 3 hours
Aviation
LPV 200 4 35*10-7 / 150 sec
Precision airport
approach CAT II/III 2.9 5*10-9 / 150 sec
Rail
Tra in c ont rol o n me di um
density lines n/a 20** 10-9 / hour
Parallel track
discrimination n/a 2.5** 10-9 / hour
*Vertical Alert Limit (VAL).
**Horizontal Alert Limit (HAL).
AV’s require
decimeter
performance, an
order of
magnitude more
stringent than
anything today
Summary
40
Highway
20 cm lat. (95%)
50 cm long. (95%)
Local Roads
10 cm lat. (95%)
10 cm long. (95%)
10 cm
Thank You
42
Tyl er Rei d
Controls & Automated Systems
Ford Motor Company
Palo Alto, CA
treid21@ford.com
Sarah
Houts
Ford Autonomous Vehicles, LLC
Palo Alto, CA
shouts@ford.com
Robert
Cammarata
Ford Autonomous Vehicles, LLC
Dearborn, MI
rcammar1@ford.com
Graham Mills
Ford Autonomous Vehicles, LLC
Palo Alto, CA
gmills47@ford.com
Siddharth Agarwal
Ford Autonomous Vehicles, LLC
Dearborn, MI
sagarw20@ford.com
Ankit Vora
Ford Autonomous Vehicles, LLC
Dearborn, MI
avora3@ford.com
Gaurav Pandey
Controls & Automated Systems
Ford Motor Company
Palo Alto, CA
gpandey2@ford.com
What is a failure?
43
Stanford Diagram
44
... Today, various companies are developing high-level selfdriving cars [1] such as Level-4 Autonomous Vehicles (AV) [2], and some of them are already providing services on public roads such as self-driving taxi from Google's Waymo One [3] and self-driving trucks from TuSimple [4]. To enable such high-level driving automation, the Autonomous Driving (AD) system in an AV needs to not only perform the perception of surrounding obstacles, but also centimeter-level localization of its own global positions on the map [5,6]. Such localization function is highly security and safety critical in the AV context, since positioning errors can directly cause an AV to drive off road or onto a wrong way. ...
... To ensure safe and correct driving, AD localization needs to not only have centimeter-level accuracy to localize the AV at traffic lane level [5,6,38], but also have high robustness under various road and weather conditions [38]. Thus, Figure 1: MSF-based localization and its use in high-level AD systems. ...
... Besides BA-MSF, we also consider two other publiclyavailable KF-based MSFs for generality evaluations ( §6.4). We follow the common parameter tuning process [66] but can only reach at most 1-2 meter accuracy, which is far from the centimeter-level accuracy required by AD systems [5,6]. Thus, in the majority of our experiments, we target BA-MSF as it is much more representative for AD systems. ...
Preprint
Full-text available
For high-level Autonomous Vehicles (AV), localization is highly security and safety critical. One direct threat to it is GPS spoofing, but fortunately, AV systems today predominantly use Multi-Sensor Fusion (MSF) algorithms that are generally believed to have the potential to practically defeat GPS spoofing. However, no prior work has studied whether today's MSF algorithms are indeed sufficiently secure under GPS spoofing, especially in AV settings. In this work, we perform the first study to fill this critical gap. As the first study, we focus on a production-grade MSF with both design and implementation level representativeness, and identify two AV-specific attack goals, off-road and wrong-way attacks. To systematically understand the security property, we first analyze the upper-bound attack effectiveness, and discover a take-over effect that can fundamentally defeat the MSF design principle. We perform a cause analysis and find that such vulnerability only appears dynamically and non-deterministically. Leveraging this insight, we design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities. We evaluate it on 6 real-world sensor traces, and find that FusionRipper can achieve at least 97% and 91.3% success rates in all traces for off-road and wrong-way attacks respectively. We also find that it is highly robust to practical factors such as spoofing inaccuracies. To improve the practicality, we further design an offline method that can effectively identify attack parameters with over 80% average success rates for both attack goals, with the cost of at most half a day. We also discuss promising defense directions.
... In a concurrent trend, increasingly-automated ground vehicles demand ever-stricter lateral positioning to ensure safety of operation. An influential recent study calls for lateral positioning better than 20 cm on freeways and better than 10 cm on local streets (both at 95%) [10]. Such stringent requirements can be met by referencing lidar and camera measurements to a local high-definition map [11], [12], but poor weather (heavy rain, dense fog, or snowy whiteout) can render this technique unavailable [13]. ...
Preprint
This paper develops, implements, and validates a powerful single-antenna carrier-phase-based test to detect Global Navigation Satellite Systems (GNSS) spoofing attacks on ground vehicles equipped with a low-cost inertial measurement unit (IMU). Increasingly-automated ground vehicles require precise positioning that is resilient to unusual natural or accidental events and secure against deliberate attack. This paper's spoofing detection technique capitalizes on the carrier-phase fixed-ambiguity residual cost produced by a well-calibrated carrier-phase-differential GNSS (CDGNSS) estimator that is tightly coupled with a low-cost IMU. The carrier-phase fixed-ambiguity residual cost is sensitive at the sub-centimeter-level to discrepancies between measured carrier phase values and the values predicted by prior measurements and by the dynamics model, which is based on IMU measurements and on vehicle constraints. Such discrepancies will arise in a spoofing attack due to the attacker's practical inability to predict the centimeter-amplitude vehicle movement caused by roadway irregularities. The effectiveness of the developed spoofing detection method is evaluated with data captured by a vehicle-mounted sensor suite in Austin, Texas. The dataset includes both consumer- and industrial-grade IMU data and a diverse set of multipath environments (open sky, shallow urban, and deep urban). Artificial worst-case spoofing attacks injected into the dataset are detected within two seconds.
... 5G and Beyond 5G systems can provide high-resolution measurements of delays and angles, which make them attractive for localization and sensing applications [1]- [4]. Localization of connected devices is important for autonomous vehicles [5], Vehicle-to-Everything (V2X) [6], [7], and spatial signal design [8]. Sensing of passive objects is important for joint radar and communication [9]- [11]. ...
Preprint
Full-text available
Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.
... To enable high-level driving automation [1], the Autonomous Driving (AD) system in an Autonomous Vehicle (AV) needs to perform centimeter-level localization of its own global positions on the map [2]. Such localization function is highly security and safety critical in the AV context, since positioning errors can directly cause an AV to drive off road or onto a wrong way. ...
Article
Full-text available
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Conference Paper
Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation.
Thesis
Full-text available
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Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. 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Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas.
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Full-text available
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Growing activity in the Arctic calls for high integrity navigation in this region. This can be achieved using Global Navigation Satellite Systems (GNSS) in conjunction with Satellite Based Augmentation Systems (SBAS) or Advanced Receiver Autonomous Integrity Monitoring (ARAIM). Single frequency GPS-only SBAS is in service in some regions today and is reliant on ground and space infrastructure. ARAIM will be more autonomous and will rely on the multitude of signals and core constellations coming in the future. Here, we examine both SBAS and ARAIM in the context of aviation and maritime requirements in the Arctic. Results demonstrate that the introduction of multi-frequency and multi-constellation to these systems enables navigation safety in the Arctic. SBAS brings aircraft precision approach as well as precise maritime operations such as mapping. ARAIM also supports precision approach in addition to autonomous ice navigation at sea but falls short of precision maritime requirements.
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