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# Digital trust for Autonomous Vehicles

Christian Laugier

► To cite this version:

Christian Laugier. Digital trust for Autonomous Vehicles. IRT Nanoelec Annual Meeting, Sep 2020, Grenoble, France. hal-03147657

HAL Id: hal-03147657

https://inria.hal.science/hal-03147657

Submitted on 20 Feb 2021

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# Confiance numérique & Véhicule Autonome

*La voiture autonome séduit ... mais inquiète aussi ! => Statut & Challenges ?*

Dr. Christian LAUGIER

*Research Director at Inria (christian.laugier@inria.fr)*

Invited Talk & Public Debate
IRT Nanoelec Annual Meeting
Minalogic Amphitheater & Internet broadcast
Grenoble, September 8th 2020
# Digital trust for Autonomous Vehicles

*The autonomous car is attractive, but it also worries ! => Status & Challenges ?*

*C. Laugier, Research Director at Inria*

* Strong involvement of Car Industry & GAFA + Large media coverage + Increasing Governments supports

* An expected market of 515 B€ at horizon 2035 (~17% world automobile market, Consulting agency AT Kearney, Dec 2017)

* Last decade: A technological breakthrough & Numerous AV experimentation in real traffic conditions

<figure><img src="image_2.png" /><figcaption>Tesla Autopilot L2 (ADAS)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>* 32 millions km covered since 2009</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>* 1st US self-driving taxi service L3 (Phoenix, since Dec 2018)</figcaption></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Drive Me trials, Volvo 2017
100 vehicles, 80km, 70km/h</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Autonomous Shuttle service</figcaption></figure>

“Self-Driving Taxi Service L3” (Waymo, Uber, nuTonomy ...)
=> Numerous sensors & Safety Driver during experimentations

* Millions of miles driven last decade... but SAFETY is still not guaranteed (Too many Benign & Serious accidents)

=> Perception & Decision-making technologies have still to be improved !!!
# Fatal accidents involving AVs – Perception failure

☐ Tesla driver killed in a crash with Autopilot “level 2” active
(ADAS mode) – May 2016

✓ The Autopilot failed to detect a white moving truck, with a brightly lit sky (Camera Mobileye + Radar)

✓ The human driver was not vigilant & didn't take over

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

☐ Self-driving Uber L3 vehicle killed a woman
=> First fatal crash involving a pedestrian
Temple, Arizona, March 2018

✓ Despite the presence of multiple sensors (lidars, cameras ...), the perception system failed to detect the pedestrian & didn't disengage

✓ The Safety Driver reacted too lately (1s before the crash)

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
# AVs have to face two main challenges

## Challenge 1: The need for Robust, Self-diagnosing & Explainable Embedded Perception

<figure><img src="image_12.png" /></figure>

**Video Scenario:**

* The Tesla perception system failed to detect the barriers blocking the left side route (no lidar !)

* The driver has to take over and steer the vehicle away from the blocked route (for avoiding the collision)

## Challenge 2: The need for Understandable Driving Decisions (share the road with human drivers)

Human drivers actions are determined by a complex set of interdependent factors difficult to model (e.g. intentions, perception, emotions ...)

* Predicting **human driver behaviors is inherently uncertain**

* AV have to reason about **uncertain intentions** of the surrounding vehicles

<figure><img src="image_13.png" /></figure>

The Telegraph

The Lexus SUV, fitted with special sensors, struck the public bus on February 14 in Mountain View, California

**Video scenario:**

Scene observed by the dash cam of a **bus** moving behind the Waymo AV

* Waymo AV is blocked by an obstacle and it decides to execute a left lane change

* The **bus driver** misunderstood the Tesla's intention and didn't yield

* The two vehicles collided
# 1st Paradigm: Embedded Bayesian Perception

<figure><img src="image_14.png" /></figure>

Embedded Multi-Sensors Perception
⇒ *Continuous monitoring of the*
*dynamic environment*

<figure><img src="image_15.png" /></figure>

Velodyne
3D lidar

<figure><img src="image_16.png" /></figure>

cameras

IBEO lidars

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

Sensors Fusion
=> Mapping & Detection

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

Dynamic scene interpretation
=> Using Context & Semantics

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

Characterization of the local
Safe Navigable Space & Collision Risk

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

## ☐ Main challenges

*Noisy data, Incompleteness, Dynamicity, Discrete measurements + Embedded & Real time constraints*

<figure><img src="image_27.png" /></figure>

## ☐ Embedded Bayesian Perception paradigm

* ✓ Exploiting the *Dynamic information* for a better understanding of the scene !!!!

* ✓ Reasoning about *Uncertainty & Time window* (Past & Future predicted events)

* ✓ Bayesian *Sensors Fusion + Scene interpretation using Contextual & Semantic information*

* ✓ Software & Hardware integration using GPU, Multicore, Microcontrollers...
# Embedded Bayesian Perception – Illustration & Valorization

=> Exploiting the *dynamic information* for a better understanding of the scene

Inria

<figure><img src="image_28.png" /><figcaption>Front camera view of the ego vehicle (urban scene)</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Classification (using Deep Learning)</figcaption></figure>

Sensors data fusion

<figure><img src="image_30.png" /></figure>

Bayesian Filtering

<figure><img src="image_31.png" /></figure>

Extracted Motion Fields

<figure><img src="image_32.png" /></figure>

1st Embedded & Optimized version

=> Patent HSBOF 2014, Inria-IRT

<figure><img src="image_33.png" /></figure>

Static part

=> Occupancy Grid

<figure><img src="image_34.png" /></figure>

Dynamic part

=> Motion fields

3 pedestrians

<figure><img src="image_35.png" /></figure>

Moving car

<figure><img src="image_36.png" /></figure>

2 pedestrians

<figure><img src="image_37.png" /></figure>

## Patented & Registered Improvements

* Inria-IRT, 2015 & 2017

* New patent filed 2020

* Industrial Licenses 2018
  (Toyota, Easymile)

<figure><img src="image_38.png" /><figcaption>Ground Estimation & Point Cloud Classification</figcaption></figure>

(patent 2017)

Detection & Tracking & Classification of Moving Objects
=> CMCDOT 2015 (including a “Dense Occupancy Tracker”)
# 2nd Paradigm: Collision Risk Assessment & Decision-making
= > Decision-making for avoiding Pending & Future Collisions

<figure><img src="image_39.png" /></figure>

Complex dynamic situation

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

Human-Aware Situation
Assessment

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

Risk-Based Decision-making
= > Safest maneuver to execute

## ☐ Main challenges

Uncertainty, Real-time ... + Continuous World changes & Unexpected events, Human in the loop

## ☐ Approach: Real-time Prediction + Risk Assessment + Bayesian Decision-making

* ✓ **Prediction:** Reason about Uncertainty & Contextual Knowledge (using History & Prediction)

* ✓ Estimate **Probabilistic Collision Risk** at a given time horizon t+δ (δ = a few seconds ahead)

* ✓ Make **Driving Decisions** by taking into account the **Predicted behavior** of all surrounding traffic participants (cars, cycles, pedestrians ...) & **Social / Traffic rules** (interactions with traffic participants)

<figure><img src="image_44.png" /></figure>

**Video: Collision Risk Assessment**

* Yellow = > time to collision: 3s

* Orange = > time to collision: 2s

* Red = > time to collision: 1s

<figure><img src="image_45.png" /></figure>
# 3rd Paradigm: Models improvements using Machine Learning

## Perception level: Construct “Semantic Grids” using Bayesian Perception & DL

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

TOYOTA

<figure><img src="image_52.png" /></figure>

NANOELEC.

## Prediction & Decision-making level: Learn driving skills for Autonomous Driving

<figure><img src="image_53.png" /></figure>

* 1st Step: Modeling Driver Behaviors using IRL

* 2nd Step:

* Predict behaviors of surrounding vehicles (using Perception & learned Behavior models)

* Make “safe & consistent” Driving Decisions for Ego Vehicle

## Open questions: Training step (Available Datasets limited), Real-time processing (difficult), Classification Errors (often not explainable), Domain adaptation (e.g. changing weather conditions)
# Concluding remarks & Discussion

* Increasing impact of AI + Real-time data processing capacity + Increased sensor performance + New Models & Embedded algorithms + Multiplication of tests in real conditions
=> *The unmanned car is gradually becoming a technological reality*

* Safety is not yet fully guaranteed !

* Current **Perception & Scene Understanding** algorithms are not robust enough for complex & highly dynamic environments

* Need to take better account of **Interactions with other road users** (using also AI approaches)

* Need to develop **Validation & Certification Tools and Methodologies** => Realistic simulators, Real-world testing protocols, Formal methods (e.g. Enable-S3 EU project & future French project Prissma)

* User confidence & Acceptance by the human society will be decisive to allow a real deployment (e.g. “cohabitation” with other users such as pedestrians, bicycles, scooters ...)

* Autonomous vehicles a priori safer than cars driven by humans (inattention).... but 0 tolerance in the event of a fatal accident involving an autonomous vehicle !

* Ethics & Responsibility issues must also be taken into account before any deployment