# Why are driverless cars still so bad at driving? | Jennifer Dukarski | TEDxDetroit

Source: https://www.youtube.com/watch?v=apPWr-jkTeQ
Recap page: https://rapidrecap.app/video/apPWr-jkTeQ
Generated: 2025-11-18T18:31:42.404+00:00

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## Quick Overview

Driverless cars still struggle with complex driving scenarios, particularly those involving pedestrians of different skin tones or the unpredictable behavior of large vehicles like taco trucks, because the AI vision systems are predominantly trained on data sets that lack diversity, leading to a 10% difference in ability to detect dark-skinned people versus light-skinned people, highlighting the critical need for inclusive design in AI training data to achieve a truly safe and accessible autonomous future.

**Key Points:**
- Autonomous vehicle (AV) vision systems show a 10% poorer ability to detect dark-skinned people compared to light-skinned people in training data sets.
- The speaker, Jennifer Dukarski, an attorney and engineer, previously worked on steering columns and seating programs for automotive technology.
- A 2017 study by Georgia Tech found that AV systems detected pedestrians 20% more accurately when they were light-skinned versus dark-skinned.
- An incident involving a Waymo driverless vehicle in San Francisco showed the car stopped completely when a pedestrian in a wheelchair and a duck were present, demonstrating system hesitation in complex, unexpected scenarios.
- The speaker advocates for 'inclusion by design,' stressing that inclusivity must be foundational, not an afterthought, in developing AV technology.
- The data suggests AV systems are more likely to detect children than adults accurately, and the data sets collected are overwhelmingly white (over 80%).

![Screenshot at 00:43: Speaker Jennifer Dukarski emphasizes that the vision systems' failure to account for diverse pedestrian demographics, such as skin tone, is a fundamental problem in achieving an inclusive future for autonomous driving.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-00-43.png)

**Context:** Jennifer Dukarski, who has an engineering degree and now works as a lawyer advising automotive companies on technology integration, presents at TEDxDetroit on the critical failures in artificial intelligence (AI) vision systems used in autonomous vehicles (AVs). She frames the discussion around the ethical and practical challenges of ensuring these systems can safely navigate real-world environments, particularly concerning diverse populations.

## Detailed Analysis

Jennifer Dukarski argues that the promise of a perfect driving future via autonomous vehicles is hampered by inherent biases in the AI vision systems. She recounts her background in automotive engineering and her shift to law, focusing on technology. Dukarski highlights a critical finding from a 2017 Georgia Tech study: AV systems are significantly less effective at detecting pedestrians with darker skin tones compared to lighter skin tones, showing a nearly 10% difference in ability, and are better at detecting children than adults. She illustrates real-world issues with examples, such as a Waymo vehicle stopping completely in San Francisco when it encountered a pedestrian in a wheelchair and a duck, indicating system failure when faced with novel or complex inputs. The core message is the urgent necessity for 'inclusion by design'—meaning data sets used to train AI must be diverse across gender, age, race, and location (urban vs. suburban) to ensure equitable safety outcomes for all potential road users, rather than treating inclusivity as an afterthought.

### Speaker Background and Motivation

- Speaker is Jennifer Dukarski, an attorney and engineer
- Previously worked on steering columns and seating programs
- Motivated by the vision of a perfect, inclusive driving future for disabled and elderly communities.

### Data Bias in Autonomous Vehicle AI

- Georgia Tech study found AVs are 10% less likely to detect dark-skinned people vs. light-skinned people
- 75% of people in the tested data sets were white, and over 80% of the detected pedestrians were white.

### Real-World System Failures

- A Waymo vehicle stopped entirely when it encountered a pedestrian in a wheelchair and a duck
- This highlights system inability to handle complex or unexpected scenarios
- The system stopped for a taco truck but did not restart automatically.

### Call for Inclusive Design

- Inclusion must be foundational in AI design, not an afterthought
- Engineers must consider location (suburban vs. urban) and diverse demographics in data collection
- This ensures the AV systems can correctly identify all people, regardless of race, age, or disability.

![Screenshot at 00:05: The opening graphic celebrating 16 years of TEDxDetroit.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-00-05.png)
![Screenshot at 00:10: Jennifer Dukarski begins her presentation on stage at TEDxDetroit, identified by the on-screen lower third.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-00-10.png)
![Screenshot at 00:36: Dukarski contrasts the excitement over driverless cars with the core issue: the bias in sensor and AI systems.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-00-36.png)
![Screenshot at 01:26: Dukarski introduces the case of Sam Schmidt, a race car driver paralyzed after an accident, to frame the importance of accessibility in AVs.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-01-26.png)
![Screenshot at 02:06: The speaker points out that Tesla's Full Self-Drive feature is not yet fully automated, leading to critical challenges.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-02-06.png)
![Screenshot at 03:38: Dukarski cites the Georgia Tech study showing a nearly 10% difference in the ability of vision systems to detect dark-skinned versus light-skinned people.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-03-38.png)
![Screenshot at 05:03: Dukarski describes an incident where a Waymo vehicle stopped completely because it detected a person in a wheelchair and a duck simultaneously.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-05-03.png)
![Screenshot at 06:15: The speaker urges the audience to look at the bigger picture, emphasizing the unintended consequences of biased AI design.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-06-15.png)
![Screenshot at 07:46: Jennifer Dukarski concludes by emphasizing the need for inclusive mobility design being integrated into engineering education and standards.](https://ss.rapidrecap.app/screens/apPWr-jkTeQ/00-07-46.png)