Driving Autonomy

Rob Weston

Machine Learning for Intelligent Transportation Systems

OXFORD ROBOTICS INSTITUTE

APPLIED ARTIFICIAL INTELLIGENCE LABA2I

…is mobile autonomy

Autonomous driving is fundamentally a systems challenge …

Why systems?

Challenges • An uncertain world • Imperfect sensors and maps • Ever changing environments (unseen locations,

weather, season)

Solutions must be… • auditable • safe • efficient (computation, energy)

Localisation Detection Tracking Planning Control

Camera, Lidar, Radar Etc.Driving commands

Systems allow us to move from this…

…is mobile autonomy

To this!

To this!

The Deep Learning Revolution

We can now accurately learn some pretty complex mappings…

… and execute these mappings very quickly indeed.

So, where does machine learning fit in?

Improving the tooling… • automatic data generation • versatile simulation, e.g.

• real -> sim • third-party behaviour

• …

Improving the platform… • sensor performance (and fusion) • situational awareness

• perception • prediction (behaviour) • planning (behaviour)

• …

The Deep Learning Revolution

We can now accurately learn some pretty complex mappings…

… and execute these mappings very quickly indeed.

Provided we have vast labelled datasets!

Integrating Machine Learning into the system creates some unique challenges

Learning from limited training labels

Adapting to new situations quickly

Managing uncertainty

Model validation

Model Size …

So where does machine learning fit in?

Leveraging the system for self-supervision…

End-to-End Tracking and Semantic Segmentation Using RNNs

Peter Ondruska, Julie Dequaire, Dominic Wang and Ingmar Posner

[Ondruska, Dequaire, Wang and Posner, “End-to-End Tracking and Semantic Segmentation Using RNNs”, RSS w/s DL in Robotics 2016]

Input Output

Deep Tracking

[Ondruska, Dequaire, Wang and Posner, “End-to-End Tracking and Semantic Segmentation Using RNNs”, RSS w/s DL in Robotics 2016]

Self-supervised learning of world dynamics (and then semantics)

Identifying Domain Knowledge What do we have? What do we know?

What do we have? What do we know?

Path Proposals for Urban AutonomyDan Barnes, Will Maddern and Ingmar Posner

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

Training is entirely self-supervised.[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

Deployed with monocular camera only.[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

Generating training dataPath Proposals

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

Generalises across appearance changes…

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

… and to new environments

[Barnes et al, “Find Your Own Way: Self-Supervised Segmentation of Path Proposals for Urban Autonomy”, ICRA, 2017]

Self-Supervised Distraction SuppressionDan Barnes, Will Maddern, Geoff Pascoe and Ingmar Posner

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

Deep Distraction SuppressionLearning where to look…

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

Deep Distraction SuppressionLearning where to look…

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

OverviewDeep Distraction Suppression (& Mono VO)

Training is entirely self-supervised. Deployed with monocular camera only.

How do we do it?Deep Distraction Suppression

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

Generating Training DataDeep Distraction Suppression

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

DeploymentDeep Distraction Suppression

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

DeploymentDeep Distraction Suppression

[Barnes et al, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments”, ICRA 2018]

Deep Inverse Sensor Modelling In RadarRob Weston, Sarah Cen, Paul Newman, Ingmar Posner

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Using Radar for Autonomous VehiclesGhost Reflections

Saturation

Cars

Speckle Noise

Free

Occluded

Noise Artefacts

Occlusion ?

Why? •Resilient to adverse weather conditions •Large range

Challenges? •Sensor Noise •Occlusion

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Project Proposal

Aim: Leverage the power of deep learning to determine what space in the world is occupied in radar

Challenges: Sensor Noise, Occlusion, Training Labels

Solution: Assume radar is inherently uncertain to account for sensor noise and train using partial labels generated in laser

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Our Approach

Use a deep Neural Network Model Heteroscedastic Aleatoric Uncertainty

Train using partial lidar labels

Desired Probability of Occupancy Grid

Raw Radar Input Lidar occupancy labels

?

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

TrainingGenerate lidar labels

Free

Occupied

Partially Observed

Unobserved

Observed

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Uncertainty: Knowing what we don’t know…

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Results ‣ Our model successfully outperforms classical CFAR filtering approaches

Raw Radar

Ground Truth

Our Approach

2D cartesian CFAR

1D polar CFAR

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

Use case: mapping

[Weston et al, “Probably Unknown: Deep Inverse Sensor Modelling In Radar”, ICRA 2019]

• machine learning does fit in (but maybe not in ways we first thought).

• systems engineering remains at the heart of deployable autonomous driving (at least for now).

• leverage the system (by definition it is all-knowing)

• be clever about how you use your data

Take aways for deployable driving solutions…

Maximum Entropy Deep IRL

[Wulfmeier et al, “Maximum Entropy Deep Inverse Reinforcement Learning”, arXiv, July 2015][Wulfmeier et al, “Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environements”, IROS 2016]

• First to frame Max. Ent. IRL in deep learning context

• Efficient large-scale learning from demonstration… • backing out observed reward structure • refining human prior

Learning to act from lots of demonstration

Learned where to drive from 25,000 real-world trajectories.

SQAIRGenerative modelling of moving objects

[Kosiorek, Kim, Posner, Teh, “Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects”, NeurIPS 2018]

• Extends AIR to image sequences.

• Learns to detect and track objects without any supervision.

• Learns object motion models.

• Obtains a richer generative model by leveraging temporal information.

Code available on GitHub

…is mobile autonomy