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International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
Optimizing ADAS and Autonomous Driving
Systems with Advanced Ethernet Protocols and
Machine Learning
Ravi Aravind
Senior Software Quality Engineer Lucid Motors
Email: raviarvind25[at]yahoo.com
Abstract: We will review how Ethernet and open standard AVB/TSN are evolving for automotive and how they offer real implementation
benefits from both a hardware and software level. We discuss how AVB/TSN IP can be deployed at the application level, making it easier
to develop, test, and optimize use cases with Ethernet as the network backbone. These can range from in-vehicle multi-resolution GUI and
multiple safety-critical ADAS to high-performance multi-camera sensing engine features. We also look at the potential for machine
learning-based implementations inside switched-Ethernet ECUs, running software that manages congestion and competes for time-critical
services with the more traditional automotive traffic. Companies developing in the in-vehicle network solution space can learn where to
appropriately position themselves in the increasingly software-designed ecosystem-driven future of automotive electronics. We show how
HW & SW developed AVB/TSN implementation, reducing the complexity of E/E architectures, resulting in a more effective ADAS and
improving road safety. It also allows OEMs, car manufacturers, and Tier 1's to rapidly deploy that system features most important to their
customers' requirements at launch. The automotive ADAS features deployment race is about to shift up a gear, enabling them to jointly
deliver vehicles with the highest driver/user acceptance and confidence in the latest ADAS features.
Keywords: Optimizing ADAS and Autonomous Driving Systems with Advanced Ethernet Protocols and Machine Learning, Industry 4.0,
Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science,
Vehicle, Vehicle Reliability
1. Introduction
Advanced Driver Assistance Systems (ADAS) and
autonomous driving functions are disrupted by a significant
increase in generated data. The bandwidth of these data flows
originates from multiple sensors and sources located
throughout the vehicle, including cameras, radars, LIDAR,
sensors, vehicle systems, and other sources. Furthermore, the
trend shows a significant increase in raw image resolution and
frame rate for automotive cameras, while similar trends in
LIDAR lead to serious bandwidth challenges. Ethernet is the
dominant in-vehicle surround sensor network, and the
industry faces a meaningful transition from traditional 100-
Mbit generic Ethernet to significantly faster and more
efficient Ethernet technology. Customers persist in having
faster, cheaper, and more automated solutions to drive down
OEM vehicle costs while staying within the same footprint
and having robust end-to-end solutions mandated by TSN
(Time-Sensitive Networking). Extreme reliability and
efficiency when integrating Ethernet into the vehicle are
mandatory. Latency density, bandwidth density, and
uninterrupted availability are critical for life-critical ADAS
and autonomous driving systems. The industry is working fast
to respond as it continues to comply with critical vehicle I/O
demands, especially from IEEE and AVB/TSN (Audio/Video
Bridging/Time-Sensitive Networking). This crisis of data
abundance triggers both solutions that can predict, manage,
and extract only the most potentially valuable data flows,
leading the automotive market towards automation with the
help of machine learning and TSN while integrating variation
in the Ethernet hardware standards of the chip, MAC, and
PHYs(1). These variations challenge Ethernet protocol and
machine learning innovators to integrate the functionality of
their solutions cost-effectively.
Figure 1: Schematic Diagram of Sensor Installation
Location
1.1. Background and Significance
Recent technological advances have increased driver
attentiveness, reduced driver stress and fatigue, and improved
situational awareness through Advanced Driver Assistance
Systems (ADAS). ADAS are also important steppingstones
in developing Autonomous Driving Systems (ADS). As many
new "smart" interactive scenarios are enabled, such as
vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-
everything, and connected vehicles, latency, reliability, and
security have become increasingly important. Traditionally,
ADAS data is communicated using a variety of
communication methods ranging from NOR flash, Ethernet,
MOST (Media Oriented System Transport), CAN (Controller
Area Network), mMOST (MOST Data Layer), and more.
However, Ethernet technology is reaching the maturity,
granularity, communication determinism, and performance
necessary for real-time multimedia data communication. To
satisfy the increasing needs of E/E architectures and to
enhance the coexistence of automotive-grade Ethernet
communication standards and consumer-oriented Ethernet,
several advanced Ethernet protocols are under study by the
IEEE P802.1 working group and the IEEE 802.1 Audio/Video
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2147
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
Bridging Task Group. In this study, we will first present the
current state-of-the-art ADAS and Autonomous Driving
Systems, and then discuss the industry trend, recent standard
modifications, and desirable communication properties of
Ethernet for automotive applications, and deal with
automotive Ethernet protocol selection issues and future
challenges.
1.2. Research Objectives
The main tasks and objectives of the research include the
following: Carry out a literature review about the state of the
art of Advanced Driver Assistance Systems (ADAS) and
Autonomous Driving (AD) trends, Artificial Intelligence (AI)
in transport systems, and existing developments in the field of
the implementation of the Ethernet protocol in automotive
systems. - Process and analyze traffic accident data, identify
prominent significance factors of traffic accidents, and define
bottleneck and trouble points in the road network. - Identify
and analyze technical obstacles to the decision to make
popular ADAS systems that ensure driving safety. - Define
the basic problems of using Machine Learning capabilities in
the automatic recognition of economic decisions, provide
more summaries, and highlight real shortcomings of this
approach. - Propose a Direction of Traffic Problems
Elimination on the Road Stretch Based on Detected
Bottlenecks for Smart Road, according to the designed
Economic Loss for Society Relative Units (ELSRU) in the
section of existing Interstate Highway Standards. - Provide
organizational proposals for the resolution of these problems
in the implementation of this type of control. - Demonstrate
the study's results, implement cost-effective features, and
evaluate this implementation. Characterize and assess the
protocol designed for the implementation of these systems.
Overall, develop the Enterprise network model of the onboard
vehicle (model demonstration at an early stage). Propose an
optimal traffic organization scheme for high load, cost, and
safety. Apply business processes and ensure a protocol for
communication of vehicle integration. The research's main
issue and end goal will be the proposal of the optimal and
economically sound introduction of the proposed intelligent
road safety management system and the Possible Gain from
Using an Infrastructure Transport System (POG FRUITS).
The base is protected from the Mathematical Model of the
Development of the Network Economy, which reflects the
dynamic effect of the formation of the priority of the
necessary control operation and the data transfer rate in the
regulatory section of the road. It can be noted that almost all
the major problems that arise in the areas are essentially the
problems of optimizing some transport networks. Transport
networks can differ in scale, scope, and function. The study
does not contain examples and does not claim to model the
entire transport network of the city or large region. The main
network that is a priority for forecasting and optimizing
dynamic development is roads within the demonstrative
company.
2. Advanced Ethernet Protocols in ADAS and
Autonomous Driving Systems
The development trend of ADAS and autonomous driving has
a long way to go before its realization. The far-reaching
development of autonomous driving technology is also
driving the development of surrounding technologies, such as
information systems, communication systems, and even
driving force electronic systems. It is an era of global strategic
challenges that are accelerating the race for full driving. In the
first section, the challenge of autonomous driving technology
was presented, as the new trends in the automotive market
related to autonomous driving, and the unique challenges of
both ADAS networks from L2+ to L3 and the futuristic
acquiring vehicle-to-everything (V2X) and driving strategies.
In the following, how ADAS Ethernet will be an option in the
not-so-far future as the network of autonomous driving will
be introduced. ADAS is currently one of the most advanced
networks in vehicles to have. The biggest challenges in
ADAS are high speed to guarantee reduced data transmission
time, simplified redundancy for better safety, coupling with
the autonomous driving network in the future for the required
resource sharing, and operational cost benefit. Until now, two
technologies, FlexRay and MOST (Media Oriented Systems
Transport, a method of transmitting), FIT (Future In-car IP
Technologies), and Ethernet-based on AFDX (Avionics Full
Duplex Switched Ethernet, a high-speed data bus located in
the avionics industry) and ARINC 664 are analyzed and
verified for the automotive environment. The results of this
analysis are poor performance behavior from memory access
conflicts among the processor nodes, and a low-performance
bandwidth with high-optical cost, respectively. All of the
above, in multiple points, together result in high Distributed-
Transmit Interval (DTI) and large data latency between many
electronic control systems and a significant negative impact
on fault tolerance and operational cost.
Figure 2: Ethernet backbone architecture
2.1 Overview of Ethernet Protocols
Ethernet has evolved over the years. The IEEE802.3 Ethernet
has been deployed on a very large scale. There are many
different forms of Ethernet for various environments,
including very high-speed (100Gbps and above) data center
or local office networks, and medium-speed (10Gbps~)
enterprise/home office networks over copper or fiber media.
GigE (Gigabit Ethernet) is very popular in IP networking and
high-speed automotive communications. The newer
generations, including 100G, 2.5G, 5G, 25G, 40G, and
100Gbps+beyond Ethernet, are mostly designed for cloud
networks, data center networks, and other very high-speed
networks. The existing MAC layers can support multi-gigabit
transmission and also full duplex in such high data-rate
Ethernet links. MAC layers have been standardized,
enhanced, and used in application-specific systems to ensure
smooth link activation, clock synchronization, error control,
and an adequate supply of buffer space at the receiver end.
The Auto-negotiation protocol has been defined and used to
exchange capabilities and set up the connection in advance.
These technologies enable reliable link synchronization and
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2148
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
error control for Ethernet transport, both in data centers and
in the automotive segment. However, this Ethernet
technology can hardly support the most advanced GPU chips
that are capable of delivering very high-rate sensing data
(exceeding 50-100Gbps) in the data centers. During the
evolution of optical Ethernet, higher-speed optical links for
data centers have been implemented through Multi-fiber and
Wavelength Division Multiplexing (WDM) techniques.
2.2 Challenges and Limitations
Despite the rapid progress in automated driving development,
the complexity of environments, harnessing the potential of
sensor and data fusion are challenging, and hence, where
problems emerge. This is mainly due to the sheer volume of
processing capability that data fusion requires, as well as the
sensor technologies' inherent limitations. External conditions
such as heavy rainfall, fog, or snowfall result in unreliable
sensor-generated data. Environmental damages (i.e., dust,
graffiti, faded lane markings, and partially visible traffic
signals or signs) may also result in degraded performance.
These aspects may severely limit the effectiveness of passive
sensors, driving advanced driver assistance systems (ADAS),
and autonomous driving systems to rely on other detectors,
which increases the risk of failures. Consequently, ADAS and
autonomous driving systems require hard real-time
performance to support fail-operational safety requirements.
To achieve this safety level, ADAS and autonomous driving
systems must classify frames from input camera sensors with
latency on the order of milliseconds, for one such application.
The per-frame latency requirements for other onboard AI
inference tasks such as object detection, object localization,
scene parsing, and decision-making are similar.
Figure 3: Object - level sensor fusion
3. Machine Learning Applications in ADAS
and Autonomous Driving Systems
Machine learning (ML) is a significant evolution of artificial
intelligence (AI). ML systems use algorithms to uncover
trends and relationships in data. Machine learning lets
systems identify patterns and make decisions and predictions
without human intervention. Machine learning models, as a
crucial key, bring decision-making capabilities to the artificial
intelligence system. Using the data-driven machine learning
model, one can tackle complex problems, such as natural
language and image recognition. Essentially, a machine
learning model generalizes patterns from the data, so it can
predict or classify future data. Machine learning is widely
used in autonomous driving technologies. Training these
machine learning models requires high processing
capabilities, which are facilitated by hardware acceleration,
such as a graphics processing unit (GPU) and GPU board.
Machine learning techniques are installed in data centers or
edge-side computing devices to train models and make
predictions respectively. In general, large training data sets
will result in high accuracy for machine learning models.
Low-latency networks and high transmission throughput are
thus important in collecting traffic data and video streams
simultaneously. Moreover, data centers are equipped with
various servers and interconnect technologies, connecting
these servers to a network switch to form a cluster. Low-
latency server-to-server transmitting technology is also
necessary to make machine learning jobs more efficient. In
this chapter, we propose edge-side computing for
implementing plant model optimization in a data center and
server-to-server network communication at the product level.
3.1. Types of Machine Learning Algorithms
ML algorithms use learning techniques to automatically learn
relationships between input and output data. The algorithms
can be used to recognize patterns, make inferences, and make
decisions or predictions without being programmed
explicitly. There are four types of ML algorithms in modular
ADAS and autonomous driving systems: supervised learning,
unsupervised learning, reinforcement learning, and semi-
supervised learning. These algorithms are applied to the
respective data either for image processing or sensing. In
supervised learning, the system presents all the features of the
inputs along with the label(s), which the algorithm will seek
to predict. The algorithm then learns how they correspond. Its
goal is to map input data to some output -- giving clear
examples of what we want to produce. Supervised learning is
often used when we want to identify objects in images, reveal
relations, segment information in sensory data, or perform
classification -- for instance, to calculate the angle. The most
common type of supervised learning is regression, in which a
model attempts to predict continuous data, and classification,
in which a model attempts to predict discrete or qualitative
data, such as night or day, pedestrian or vehicle. In
unsupervised learning, the system is only provided with input
data, and the system is not presented with any labels. It finds
patterns in data. This approach tries to expose the underlying,
or latent, structure in the dataset. Clustering, where
observations are grouped within a dataset, and generative
models, where a model learns about unspecified features
within the data, are examples of unsupervised learning.
Reinforcement learning is a type of semi-supervised learning.
In this, the learning system is provided not only with input
data but also with feedback in the form of trial and error, the
result of the generated action. Semi-supervised algorithms
include a mixture of labeled (fully supervised) and unlabeled
(unsupervised) examples.
Figure 4: The illustration of ADAS
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2149
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
3.2. Benefits and Challenges
While Ethernet is the technology of choice for the backbone,
integration of all ECUs into an Ethernet backbone presents a
complex multi-dimensional challenge due to the need to
combine the seemingly heterogeneous. These include audio
and video signals, control and diagnostic signals, active safety
and autonomous driving data, standard networking data such
as vehicle health reports, OBD (on-board diagnostics),
802.1AS and 802.1AS-Rev keeping real-time data
synchronized between cameras, lidar, and radar, and
gigabytes of software updates during vehicle assembly.
Furthermore, these signals need to be transported among
various communication mediums, such as automotive
Ethernet, coax, LVDS, and PCIe. The capability of Ethernet
to transport all these types of signals can bring a vehicle from
assembly and diagnostic to manufacturing automation and
repair to an ultimate ecosystem: AI computation, autonomous
vehicles, and vehicle-to-anything (V2X) will become reality
soon.
The Ethernet throughput can easily be scaled with multi-lane
transceivers, creating additional opportunities such as
eliminating expensive SerDes and creating innate functional
safety FPGA. However, ADAS Ethernet presents a unique set
of challenges. Significant among these are SWaP (size,
weight, and power), compatibility with current automotive
technologies and infrastructure, performance, reliability, and
security. Providing broadband coverage throughout the
vehicle requires various levels of aggregation while
addressing the SWaP concerns. ADAS must also be fully
compatible with the legacy 1000BASE-T1 standard and other
corresponding speed automotive Ethernet standards while
also capable of connecting to the future 10Gbps on-board
system and off-board operations as outlined in Table I. Full
benefit of multi-lane capabilities of Ethernet will only be
realized with multiple remote devices, separated by as much
as 15 meters each, connected to a single switch. All these
Ethernet protocols need to provide error-free communication
with the support of hardware processing while meeting the
intrinsic requirements of a mission-critical automotive
environment such as message prioritization, low latency,
frame and rate control for lanes, clock and data recovery, and
deterministic arbitration.
Figure 4: Internet of Vehicles communication
4. Integration of Machine Learning with
Advanced Ethernet Protocols
Developing ADAS and autonomous driving systems requires
keeping development and vehicle testing projects running
simultaneously. Recent breakthroughs in high-performance
computing (HPC) and infrastructures have yet to be applied
in this context. In time-correlated HPC, enough configuration
parameters are being developed and tested. The complex
interdependence of present operations with the underlying
vehicle infrastructure leads to tightly coupled participant,
component, and system behavior. Designing cooperative
objects in coordination with the infrastructure will produce
greater performance improvements for HPC applications than
approaches that target the optimization of individual system
components in isolation. Computer scientists are now
beginning to address these challenges. The participants
introduce the tight integration of a lot of areas of study
covering short-term system investments, but they are vital for
long-term meaningful performance improvement.
Automotive Ethernet is purpose-built for the automotive
environment. Concepts such as in-vehicle data logging are
becoming increasingly important in the automotive industry
due to the extensive development and testing efforts
necessary for ADAS and autonomous driving (AD) systems.
During these development and testing efforts, the system
components are closely coupled. Designing cooperative
objects in coordination with the infrastructure will produce
greater performance improvements for HPC applications than
approaches. Computer scientists are now beginning to
address and contribute to achieving such continuous data
collection, raw data analysis in real-time, and concrete
knowledge of the consequences of tuning parameters of
projects. The ultimate goal of the prototype is to help
dealerships automate operations typically involving the client
waiting for the vehicle. The prototype of a system to manage
the vehicle reception scheduling in Opel Authorized
Workshops covered the main requirements designation in the
automotive field. The concepts for the service,
manufacturing, and standby production were implemented in
architecture, but capable of capturing and analyzing real
communication data. The system learns the new requirements
of the area of interest or application (service, manufacturing,
and standby production) in real-time and updates the classifier
tool automatically.
Figure 5: Machine and Deep Learning algorithms workflow
in medical image
4.1. Opportunities for Optimization
While objective testing and validation of complex advanced
driver assistance systems (ADAS) and autonomous driving
systems are still in the early development stages, the
challenge and promise of these systems continue to grow.
Like the aerospace industry, the ability to test and optimize
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2150
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
these systems will greatly enhance their safety, security, and
acceptance. We see a wide variety of opportunities for
advanced data analysis of datasets in most development
efforts. Some of these opportunities focus on the basic usage
of commercially available techniques on richer or more
diverse sources of data, while others, particularly those in
which benefits are shown in particular domains or are enabled
by particular kinds of ADAS data, require the development of
new data analysis techniques. There are several ways that
high-quality machine learning can facilitate the design and
refinement of ADAS and autonomous driving systems. By
utilizing machine learning, users can find the connections in
driving data that allow for better system design. They can also
utilize machine learning to predict how different system
designs will work. Lastly, machine learning techniques can
be used to create complex test cases from the data. In the
automotive industry, an area where we expect the investments
in machine learning applied to ADAS datasets to focus on and
will benefit from is test cases. We will choose test cases that
are both complex and meaningful and also prove test case
completeness by automating most of the testing process. By
doing this, we will be more comprehensive while also
enabling more efficient and effective system development
over typical heuristic approaches.
4.2 Case Studies
Even with advanced node behavior and self-optimization
algorithms, the network control plane is designed based only
on booking patterns and threshold-based triggers. Any change
in the mapping between business importance and network
behavior involves specialized domain knowledge and
configuration changes in each of the multiple network nodes.
In an FPGA-based solution, specialized application
knowledge allows FPGAs to offload control. A Sockets API
is introduced into the data plane with typical SDN semantics.
For typical network functions of routing, NAT, and
firewalling, 10 to 100 k routes can be maintained with a set of
fully associative Ternary Content Addressable Memory-
based pipelines. For each pipeline, two levels of hashing
allow an even distribution of rules. In contrast, the Data Plane
Development Kit (DPDK) is targeted for rule lookup and
rule-based actions of 10 to 20k operations per second for user-
defined traffic categorization and has been used successfully
in a privacy-preserving network.
For rules requiring only a single level of lookup, structuring
memory for with-protocol header bit extraction minimizes the
number of IP packets needed to reach a cache hit in typical
scenarios. Shifting from binary match to distance-based
packet classification, or using a bloom connector in the first
place allows the flexible network nodes to accommodate
flexible QoS, dynamically adjusting response times according
to state and business policy. Positional encoding architecture
is a novel accelerator designed for processing the large-scale
rule set with a two-level bitmap array that can achieve full-
match operation. Another work proposes a hardware-based
acceleration scheme using FPGAs to accelerate classification,
counting, filtering, and caching operations for network
monitoring. A coarse-grained net cache pattern is hidden in
long flows for multipath routing. The full-match acceleration
took only 0.3% of the FPGA's slices, 76% of the read, and
4.2% of the write resources. The counting and filtering
operations took 10%, 37%, 2.4%, and 29% slice, read, write,
and LUT resources.
In general, simply matching the network node's throughput to
the interface's bottleneck is also not a silver bullet. With rigid
control implemented in the network node control plane, the
rapidly changing control plane service states lead to
oscillatory bandwidth provisioning, interface over
subscriptions, and long-tailed end-to-end service response
time have already been discussed. Brute force schemes
merely tailor the account's service level to match the peaks of
the possible overbilling dipping valleys of an unfriendly fair
admission controlling a cloud service node's microservice
monetization access.
Highly connected networks such as data centers with rapidly
fluctuating demands can have Amdahl's law and crossover
effects reduce aggregate numbers of microservices that
process requests concurrently, introduce contention costs that
are larger than those of directly sharing CPU and other
resources, longer tail latency and unfair CPU allocation, and
validate the internal monitors' jurisdictional claim of an
implicit right to access and control resources, queries, and
packet-dumping on the segments of the PCIe tree. In a design
with Tofino-like control, the overall response time is
proportional to the slowest control plane stage's TTL with a
replicated parallel DAG dealing with the fractional request
rate, multiple frontend weights, and a shadow backend.
Figure 6: TSN Switch
5. Conclusion
The name of the game in vehicle design is creating reliable
and safe systems while keeping down costs. Ethernet- and
AVB-based protocols can help do this for ADAS and
autonomous driving systems by adding redundancy and
failover features in the Ethernet link and by delivering reliable
multimedia entertainment distribution in a standard way that
is not susceptible to hacks. Machine learning algorithms help
to reduce tooling costs, enabling systems to be developed with
low chip count As-Is protocols and off-the-shelf Ethernet
devices. Chip builds assist from large chip providers frees up
some FPGA space for user proprietary data, facilitating
customization that could give vehicles of a specific
manufacturer some unique features.
Combining AVB, TSN, Ethernet, and RTP hardware assists
in one device and opens up attractive options for the designer.
With the end of life of traditional automotive infotainment
components that delivered similar capabilities and proprietary
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2151
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
car OSS systems that support more built-in features like
vehicle-to-anything (V2X), V2V, or vehicle-to-charging
(V2C) communication, implementations using Ethernet will
become increasingly viable and should not be dismissed by
designers.
5.1 Key Findings and Contributions
This chapter explored the utilization of automotive software-
defined networks to enhance reliability while reducing the
heavy usage of networks introduced by Ethernet concepts.
EM is a cutting-edge communication technology that may
provide high reliability. To overcome the problems which had
been faced by ADAS and autonomous driving systems using
extensively used technologies or concepts, this chapter has
made several contributions. First, considering from a
software-defined network perspective, we use an Ethernet
concept known as TSN. This can bring reliability to the
system, according to two recognized challenges in the ADAS
and autonomous driving architecture. The second aim of
unbalanced network traffic allocation was to create an
intelligent speed reduction mechanism. The system may
instantly adapt to different vehicle-driven networks. The
automobile may continue to communicate regardless of the
degree of performance with other systems that require low
latency. This is made possible through adaptable vehicle-
based speed control.
The third aim of this chapter was to give a possibility to road
maintenance services, communication between vehicles, and
data uploading for independent vehicles, and different types
of entities. We examined frequent heavy network bandwidth
usage scenarios for communication among vehicles, and
between vehicles and the edge cloud, leading to large data
traffic. Our method does not alter the specific problem faced
by the task distance-based semiconductor concept. It does put
the matter into perspective. It demonstrates the feasibility of
providing various types of network data centered on the edge
cloud. As a result of different problems confronted by the
found difficulties, these provisions have provided a
comprehensive technique for autonomous who wish to solve.
Our approach and findings were evaluated using simulation
data. This established the efficacy of our approach.
5.2. Future Research Directions
With the increasing amount of data being generated in AD
and ADAS systems, it is becoming a challenge to manage,
store, and process this enormous amount of data. The data
generated in ADAS and AD is not just of high velocity but is
also of high variety and unstructured. Machine learning, deep
learning, and computer vision play a critical role in
transforming the data into useful information for various
applications in ADAS and AD. Shortly, machine learning and
computer vision techniques will increasingly automate the
data analysis step and minimize human intervention. The role
of machine learning will expand from classifying high-
resolution images to safe decision-making based not only on
sensor data but also on a massive amount of historical and
geographical life cycle data such as traffic patterns, vehicle
behavior, weather data, safety history, traffic light
information, pedestrian pathways, etc. Dynamic machine
learning models are of critical importance for AD and ADAS,
as the system must learn in real time and adapt to changes in
its surrounding environment. Existing machine learning
models may also need a new form of explanation and
reasoning, motivated by safety regulations and human trust
factors.
Automotive-grade Ethernet is a key enabler of high
performance and scalability in ADAS and AD systems.
Automotive-grade Ethernet enhances the reliability in driver
assistance and autonomous driving systems by providing low
latency deterministic performance, reliability, high
bandwidth, advanced security, etc. Current works on
automotive-grade Ethernet lack focus on data quality. It is
important to analyze how the AI models to be trained on this
large volume of data can benefit from higher quality of data
and latency and bandwidth improvement that Automotive-
grade Ethernet provides. The focus of the current industry is
on achieving the lowest end-to-end latency for a specific
traffic profile between specific end stations instead of
allowing an absolute maximum bandwidth to be consumed by
each station. These requirements for high bandwidth should
also be addressed by the ongoing standardization of 25, 50,
and 100 Gbps Ethernet connectivity, besides the
standardization of time-sensitive networks for automotive
applications. In the current state of automotive applications,
there is minimal and no standardized traffic control in the
network device such as a switch. Further research should
focus on the hardware support required for the new entrance-
class time-sensitive networking standard so that automotive-
grade Ethernet can be cost-effective. Shortly, we will have
more efficient and optimized solutions leading to the
deployment of vehicle-to-vehicle and vehicle-to-
infrastructure communication. This vehicle-to-everything
(V2X) communication is of much importance in advanced
driver assistance systems since it is the key to the
enhancement of safety and the implementation of cooperative
driving functionalities. Automotive-grade Ethernet will help
improve V2X communication and information sharing,
paving the way for cooperative intelligent transport systems.
In conclusion, more research would be needed to further
optimize and improve automotive-grade Ethernet. A set of
specific, yet broad set of open research issues outlined here; a
polyvalent and also broad list of potential research initiatives
in the domain of transportation and automotive networks can
be addressed using the Ax infrastructure.
Figure 7: Future Research Direction
6. Conclusion
Building an electronic end-to-end system with ECUs
(engines/actuators connected to multi-sensors), featuring high
Paper ID: ES24611084428
DOI: https://dx.doi.org/10.21275/ES24611084428
2152
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 12 Issue 10, October 2023
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
bandwidth, low latency, high reliability, safety compliance,
security, and low power consumption, is the major challenge
for the ADAS and autonomous driving. In the 21st century,
the transportation industry has somewhat stagnated, as
reinforced concrete, steel, high explosives, and fuel are still
the main technologies over the last century. Therefore, long
traveling times, traffic accidents, dynamic traffic jams, and
cost-ineffective drive transportation are in a new era to solve
these issues: ADAS and Autonomous Driving. With the
development of infotainment, the automotive infotainment
use of Ethernet continues to grow its networking scope.
In this section, we proposed an advanced Ethernet
communication network intended to work in the TSN context
and have been designed and characterized with the FPGA
solution implemented in the end devices (SoC: CPU and
FPGA). Our solution is connected through 100Base-T1 with
the sensors and 1000Base-T1 with the advanced ECUs
(ADAS). With time to revoke, the Ethernet network inferred
by FPGA with time-aware auto-negotiation is fully agreed
with the TSN standard IEEE 802.1Qbv and 802.1Qbu to
support the management use cases and safety priorities.
Finally, we proposed a Machine Learning approach that
vectorized one ATL transformation rule and classified it as
Safe or Unsafe from a training base produced from
simulations launched with a design of experiments OpCode
injected in the RTL model. To avoid recalculating the power
tool on FPGA each time a change in the TSN core is made,
we tested the validity of the learning approach with small
changes in the ATL transformation rule from the training
base. A line reduction and an extension of models and test
bench will be the subject of interdisciplinary FPGA/ART
benchmarks shortly.
6.1 Future Trends
Automotive Ethernet will be key to the integration of
infotainment, advanced driver assistance systems (ADAS),
and autonomous driving (AD) systems in HD vehicles. Time-
Sensitive Networking (TSN) allows this by implementing
determinism and quality of service (QoS) in data
transmission. AVs will use 10Gbps Ethernet, and in-vehicle
environments will be connected to 2.5 Gbps infrastructure
and 1 Gbps switch ports to replace today's legacy networks.
AVs will incorporate IEEE P802.1Qcc Stream Reservation
Protocol (SRP), 802.1Qbv Time-Aware Shaper, and
802.1Qbu Per-Stream Filtering and Policing in bridged and
virtual local area networks. Autonomous systems will add
15% more time-sensitive data flow to the network, hence
larger switches and augmented networks. Time-sensitive
WAS-WAP integrations require SAE AS6803 Augmented
Ethernet/Front Wires within the auto drive vehicle
environment. AD multi-homed high-speed Ethernet
controllers should communicate with N (parallel visions)-LP
(3D LIDAR Predictive Map) auto-associative memory deep
learning perception accelerators (PCPAs) using small-world
switches while prioritizing visual cues over the background.
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