7 Data Challenges in Autonomous Driving
By: Nitin Kumar
Tags: Big Data, Autonomous Cars, Business, Selfdrivecarsmauritius, Innovation

Image created by author using Pixabay

Understanding Data Challenges in the Autonomous Driving Domain

There are numerous data challenges in the autonomous vehicle value chain, a lot of it impacts specific use cases and outcomes. This article highlights the nature of challenges and their impact across the data value chain.

Introduction

Automotive data is of high interest due to the diverse and complex nature of data, its multiple sources as well as high volume of the data continually generated. Within an autonomous driving context, real-time requirements around navigation, safety and revenue models create new challenges.

General AV Data Challenges

As vehicles advance to higher levels of autonomy, the deep learning models utilize high volumes of data for decisions e.g., from sensors, cameras, lidars, pedestrian behaviors, road conditions etc. There are a multitude of different challenges when it comes to the data value chain within autonomous driving and we will examine a few key themes here.

AV business models are driven by the ability to understand and overcome data challenges.

Real-time and Scale Challenges

A fleet of just a few dozen cars with cameras will generate more than a million hours of video in a few months which, once captured, needs to be transmitted, stored, tagged, and processed for training via deep learning neural networks. Dodging every object, pedestrian, car, obstacle etc. can become a mammoth task if data is not decomposed in a modular way. Huge data sets e.g., driving conditions, weather, behaviors, local laws etc. and extreme computational and power requirements create challenges to operate in real time.

Debugging/Troubleshooting Challenges

Massive data sets, complex weighted features, non-deterministic AI models and real time processing is

a mix that is difficult to diagnose and interpret. Quality, completeness, interdependencies, and correlation of data including coverage of all outlier and corner use cases, are much harder to detect, predict and correct if machine learning models fail. Neural networks can become unreliable and sensitive to changes e.g., Changing of lighting conditions, resizing of images, cropping at different angles making it hard to address all safety aspects in a standardized way and to troubleshoot them in real time.

Accuracy Challenges

Safety systems require real-time detection, accuracy from non-deterministic AI models with complex compute and communication constraints — these requirements can strain the systems of today. These systems are expected to accurately operate across diverse weather conditions, visibility, infrastructure quality and external pedestrian behaviors.

Lifecycle Challenges

The scale, accuracy, logging, monitoring, reporting of data must consider the entire data management lifecycle i.e., creation, acquisition, collection, modification, processing, transmission, sharing, storage, and disposal. Given nonstandard formats of data collection e.g., differences between camera and lidar, proprietary analysis by companies across the value chain, volume, and velocity of data — we risk losing out on many important attributes of data towards completeness i.e., perishable insights or dark data.

Collaboration Challenges

The lack of standardized live and test data sharing between AV companies, infrastructure companies, regulators and other stakeholders who want to control their narratives, limit proprietary information, expand competitive advantages and not incur costs to share, makes it harder to inform and develop safety features quickly and with repeatable results.

Other Miscellaneous Challenges

1.Optimal synchronization of simulation and real data from the field, replicating driving algorithms and vice versa

2.Handling operational and supply/demand data to maximize revenue while minimizing dead miles e.g., automotive health, battery levels, fleet management and fleet positioning data etc.

3.Passenger real-time data during each of the four critical rideshare phases (pre-board, boarding, transit, disembarking), to ensure passenger safety and health, payments, luggage, child safety etc. require complex and secure interplay of real-time decisions from sensor data, 3rd party integrations

4.Aggregating and managing real time data from the ecosystem i.e., vehicle edge and cloud to monetize mobility services through APIs

The solution to these data challenges is being focused on the specific autonomous use case being addressed — and not let the large amounts and velocity of data overwhelm the system. Specific use case focus enables decisions on value of data, reduced scope, addressing challenges in a more surgical manner and will help with architectural, partnership, safety, regulatory decisions that will ultimately result in a valuable and profitable autonomous service.

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AV Data Value Chain Challenges

Each of these data challenges manifest themselves differently across the autonomous driving data chain. The table below attempts to map challenges to the data value chain. The challenges with accuracy have the highest impact across the value chain all challenges impact AV decisions. Figure 1 below provides a summary of the data challenges mapped to use cases.

AV Data Challenges Mapping (Framework by Authors)

Conclusion:

Automotive and autonomous data is diverse and challenging to manage since it is used for real time and safety critical decisions, and since the data has impact across a wide variety of use cases. The point to note is that the challenge is not a lack of data, but understanding data, its challenges and effectively prioritizing AV data requirements and system design to operationalize specific autonomous use cases.

Other Relevant Articles:

1. The Autonomous Driving Technology Stack

2. Autonomous Driving: Why Big Data Value is a Myth

3. Seven Data Challenges in Autonomous Driving

4. Seven Autonomous Driving Business Models

5. 12 Technologies Enabling Autonomous Driving Business Models

6. Pathways to Adoption For Autonomous Driving Infrastructure

About the Authors:

Nitin Kumar

Nitin Kumar is a 20-year veteran in the Hi-Tech industry. He is currently the CEO of Appnomic but played a variety of hands on executive roles ranging from CEO, Chief Growth Officer, Chief Transformation Officer, M&A Integration/Separation Leader, BU Head and Management Consulting Partner (corporate and PE portfolio companies). Nitin Kumar is a member of the Forbes Technology Council and shares his ideas and thoughts on the forum regularly. In his role as a former Management Consulting Partner, Nitin has done multiple strategy and M&A engagements for Software, Hardware, Semiconductor and AutoTech sectors gaining invaluable insights in the value chain, technologies, and business models. He is also a Certified Autonomous Driving Professional.

Manu Namboodiri

Manu Namboodiri has for 20 years worked across industries such as autonomous vehicles, security, IoT, software and has broad experience ranging from strategy, product, marketing, and ecosystem development. He resides in the San Francisco Bay area and advises companies in various stages of market adoption.

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