an innovative traffic management scheme for deterministic

An innovative traffic management scheme for deterministic/event-

based communications in automotive applications with a focus on

Automated Driving Applications

Giancarlo Vasta, Magneti Marelli, [email protected]

Lucia Lo Bello, University of Catania, [email protected]

October 10, 2018

2018 IEEE Standards Association Ethernet & IP @ Automotive Technology Day

Presentation outline

October 10, 20182018 IEEE Standards Association Ethernet & IP @ Automotive Technology Day

■ Event-driven traffic in automated vehicles

■ Support offered to event-driven traffic in TSN standards

■ A novel approach to deal with event-driven traffic

■ Performance evaluation

■ Automated Driving application

■ Summary and conclusions

2

The need for Event-driven Traffic Support


Fact 1: The external world in which the cars move is non-deterministic

Unpredictable events may occur and determine unforeseen situations.

Fact 2: Autonomous vehicles need to properly react to such situations

The communication system has to support event-driven transmissions with

minimum delay.

Question 1: How to handle event-driven

traffic with real-time constraints?

Question 2: How and under which

conditions is it possible to guarantee

a latency bound for event-driven flows?

Let’s have a look on the TSN standards…

3

TSN support for Event-driven Traffic


Fact 3: TSN standards offer a limited support for event-driven real-time

traffic

� Some shaping mechanisms have been defined, but

� they do not care much about the latency of event-driven traffic

or

� they can be complex

� The easiest way to deal with event-driven traffic is to serve it in the Best-effort

class, but

� no guarantees, even in the case the event-driven traffic has a known

minimum inter-arrival time, can be provided.

4

This presentation is about


An innovative traffic management scheme for IEEE 802.1Q bridges and end

nodes that

� introduces explicit support for the Event-Driven (ED) real-time traffic

� enables the transmission of ED traffic over TSN networks while

● maintaining the support for Scheduled Traffic

● providing delay guarantees to the Stream Reservation classes.

The proposed approach:

� works at the MAC layer

� relies on the forwarding mechanisms defined in the IEEE 802.1Q-2018

standard, with suitable modifications

5

System model


Traffic type Characteristics

Scheduled traffic

High priority real-time traffic

transmitted according to a

time schedule (time-driven)

with no interference from

other traffic.

Stream Reservation Periodic, guaranteed

Event-driven traffic

Aperiodic bursts, generated

by events, with real-time

constraints

Best-effort traffic No guarantees,

performance is statistical

6


Main features

� Scheduled Traffic is handled through the gate mechanism defined in the IEEE

802.1Qbv

� ED bursts are handled with a novel traffic management

Low latency for ED flows is achieved

� If the minimum interarrival time for ED bursts and the maximum burst size are

known, the maximum E2E latency for ED traffic is bounded and can be

calculated.

� E2E latency for SR Class A and Class B are bounded and guaranteed.

� Feasibility: The proposed approach can be implemented on devices that

support TSN standards. No special hardware modifications to the standard

devices are required.

Advantages of the proposed solution

7

Simulations

Simulations were run using the OMNeT++ and the INET framework, modified

so as to model the TSN protocols (i.e., IEEE 802.1Q-2018, etc.)

Aims

• Evaluate the E2E performance of the proposed approach in realistic

automotive scenarios

• E2E Latency performance with different data rates (i.e., 100Mbps, 1Gbps,

10Gbps).

October 10, 20182018 IEEE Standards Association Ethernet & IP @ Automotive Technology Day 8

The proposed solution was compared with an alternative approach, that

handles the ED traffic as best-effort, in the highest priority queue for that class.

ED TrafficED Traffic

Alternative approach

ST


Simulations

TSN Switch

ECU

• Each node transmits sensor data to the ECU

• 3 configurations:

• 100Mbps: 30 fps compressed video streams,

relative deadline 33ms.

• 1Gbps: 60 fps compressed video streams,

relative deadline 10ms .

• 10Gbps: 30 fps uncompressed video streams,

relative deadline 10ms .

Node

- Radars

- Lidars

- Ultrasonics

- Cameras


Simulation scenarios

Flow Number of flows Total MAC Bandwidth

Lidar 4 0.93 Mbps

Ultrasonic 4 0.23 Mbps

ADAS Sensors 4 Max 34 Mbps

30 fps compressed Video 4 ~52 Mbps

Case A: 100 Mbps Scenario – ADAS


Lidar 4 0.93 Mbps



60 fps compressed Video 4 ~103 Mbps

Case B: 1 Gbps Scenario – ADAS


Scenarios


Lidar 4 0.93 Mbps



30 fps uncompressed Video 4 ~7 Gbps

Case C: 10 Gbps Scenario – Automated Driving


Scenarios

Flow Parameters and Mapping

Flow Length Rel.

Deadline

Sampling

Time

Arrival

pattern

Traffic Class

Lidar 250 B 10 ms 10 ms Periodic ST

Ultrasonic 100 B 20 ms 20 ms Periodic ST

ADAS Sensors ~10 KB 10 ms [10-100] ms Event-driven ED/BE

30fps Video - ADAS

(100 Mbps)~43 KB 33 ms 33 ms Periodic SR_A

60fps Video - ADAS

(1 Gbps)~43 KB 10 ms 16 ms Periodic SR_A

Raw 30fps Video –

Automated Driving

(10 Gbps)

6.9 MB 33 ms 33 ms Periodic SR_A

Class A video frames are segmented in small Ethernet frames, each one

transmitted by the application every 125us.

Performance metrics: End-to-end latency, defined as:

reception time – generation time (measured at the application)


Event-driven flows

The proposed approach

- halves the maximum E2E latency

- reduces the average message latency by 870us

5119

19742332

1104

0

1000

2000

3000

4000

5000

6000

Max E2E-Latency Avg E2E-Latency

us

ED Flows: Message latency

Alternative Approach Proposed Approach

SR 52%

ED 33%

ST 2%

Unused 13%

Case A: Workload vs. bandwidth


Simulation Results – Case A: 100 Mbps scenario

Event-driven flows – End-to-end latency distribution

Alternative Approach

Proposed Approach

The ED traffic with the

proposed approach obtains

• lower latency values

• lower latency variability


Simulation Results – Case A: 100 Mbps scenario

Simulation Results – Case B: 1 Gbps scenario

Event-driven flows

The max latency of event-driven messages is almost the same in both configurations.

Reason:

Very low workload, the SR workload is 11% of the available bandwidth.

SR 11%ED 3%

Other 0,20%

Unused 86%

Case B: Workload vs. bandwidth

218

111

192

105

0

50

100

150

200

250


us




Simulation Results – Case B: 1 Gbps scenario


Proposed Approach

Alternative ApproachThe ED traffic with the

proposed approach obtains




Simulation Results – Case C: 10 Gbps scenario

Event-driven flows

Under high SR workload (69% in this scenario)

the proposed approach significantly reduces the

latency of event-driven traffic.

SR 69%

ED 0,33%

Other 0,02%

Unused 31%

Case C: Workload vs. bandwidth

63

35

20

15

0

10

20

30

40

50

60

70


us




Simulation Results – Case C: 10 Gbps scenario


Proposed Approach


The ED traffic with the

proposed approach obtains:




Other traffic classes – Case A: 100 Mbps scenario

31332 32466

0

5000

10000

15000

20000

25000

30000

35000

AppMessage Max

us

Video Flows: Max latencyper video frame


• Scheduled traffic is unaltered.

• SR Class A traffic (i.e., video streams) meets the relative deadline of 33ms

per video frame with both approaches.

Rel.

Deadline

33ms50

26

50

26

0

10

20

30

40

50

60

70

80

90

100

Lidar Ultrasonic

us

ST Flows



Other traffic classes – Case A: 100 Mbps scenario

Video flows (SR_A) – End-to-end latency distribution


Proposed Approach

• The proposed approach obtains a

longer tail, but the difference between

the maximum values is around 3%

and affects only 0.3% of the video

frames.

• The relative deadline (33ms) is met in

both cases.


Other traffic classes – Case B: 1 Gbps scenario



per video frame in both approaches.

7993 8132

0

2000

4000

6000

8000

10000

AppMessage Max

us



Rel.

Deadline 10

ms9,68

7,28

9,68

7,28

0

2

4

6

8

10

12

14

16

18

20

Lidar Ultrasonic

us

ST Flows



Other traffic classes – Case B: 1 Gbps scenario

Video flows (SR_A) – End-to-end latency distribution

Proposed Approach


The proposed approach

obtains slightly higher latency

values and variability for SR

flows, but the difference is

minor.


Other traffic classes – Case C: 10 Gbps scenario



per video frame in both approaches.

31005 31005

0

5000

10000

15000

20000

25000

30000

35000

AppMessage Maxu

s



Rel.

Deadline 33

ms5,65 5,415,65 5,41

0

1

2

3

4

5

6

7

8

9

10

Lidar Ultrasonic

us

ST Flows



Other traffic classes – Case C: 10 Gbps scenario

Video Flows (SR_A) – End-to-end latency distribution

Proposed Approach


Plenty of bandwidth, so no

difference between the two

approaches.



� The proposed approach provides event-driven traffic with

� very low maximum latency values

� very low latency variability.

� In addition, the proposed approach

● does not affect Scheduled traffic

● has a very limited impact on the E2E maximum latency for Stream

Reservation traffic

Summarizing

26

Application on Autonomous Driving: Smart Corner™

27October 10, 20182018 IEEE Standards Association Ethernet & IP @ Automotive Technology Day

Automated Driving architecture


Lidar

Radar

Camera

UltraSound

Component

/ HW Layer

Function / SW Layer

V2X Contr. Unit

Automated Driving

Domain Controller

Se

nso

rs

Sensor specific

ECU (?) *

PWT ECU

Steering ECU

Brakes ECU

Suspension ECU

GNSS Receiver

Environment

Data Sensing /

Receiving

Environment

Data Sensing /

Receiving

Object

Detection

Object

Detection

Sensor

Fusion

Sensor

Fusion

MappingMapping

Data Fusion /

Environment

Model

Data Fusion /

Environment

Model

DecisionDecision

Motion & Actuation

Planning & Control

Motion & Actuation

Planning & Control

ActuationActuation

Understanding the

context around

the vehicle

Planning and

executing

motion

Perception Think & Decision Actuation

(Device specific

driver and SW)

Int and Ext.

V2X Led Lights

* Sensor specific ECUs used instead of Automated Driving Domain Controller for Individual ADAS functions

Magneti Marelli Autonomous Vehicles


Autonomous Vehicle for

Parking scenario (level 4)


Highway scenario (level 3)


Urban scenario (level 4)

Autonomous Vehicle

an innovative traffic management scheme for deterministic

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