Drive into the Future: Unleashing the Power of Driver Monitoring with Machine Learning Magic!

Buckle up for a ride through the evolution of safety tech – from Adaptive Cruise Control to Lane Departure Warning with a touch of lane-keeping assist and Automatic Emergency Braking! While these systems have slashed accidents, there's still the challenge of predicting the driver's next move. We're on a mission to solve this puzzle and make safety systems as intuitive as your favorite playlist. How? By diving into the world of driver monitoring and sprinkling some machine learning magic!

The deployment of active safety functions, such as Adaptive Cruise Control (ACC), Lane Departure Warning (LDW) with Lane Keeping Assist (LKA), and Automatic Emergency Braking (AEB), has substantially reduced the number of accidents in the traffic environment. Such systems predict potential collisions or hazardous situations and automatically steer or brake the vehicle to a safe state. However, making predictions about the future is challenging, and implies that the safety system has to estimate the driver's intention. The naive approach for a safety system is to make assumptions about the driver’s intention, or completely neglect it, which often leads to a misalignment between the safety system and the driver, i.e., the safety system may trigger automatic maneuvers that the driver perceives as unwanted.

Recent technology advancement has made it possible to use cameras to monitor the driver, which is expected to contribute valuable information to better understand the driver's intention. This thesis aims to explore how driver monitoring can help us improve active safety systems to be intention-aware by using a machine-learning-based approach.

Project Description: 

In this master's thesis, your quest involves:

1. Analyzing real-world data for critical traffic insights.
2. Decoding driving behaviors revealed by driver monitoring.
3. Developing a machine learning model for accurate high-level driving goal predictions.
4. Crafting results into a thesis that's informative and impactful

Our Contribution:
We offer real-world challenges and supervision from seasoned developers and researchers.

Qualifications:
Seeking curious minds with backgrounds in electrical engineering, computer engineering, control theory, or physics. Familiarity with supervised learning, scientific research, and Python is advantageous.

Ready to Drive Progress?

We encourage you to send in your applications, including your CV, a motivational letter that speaks to your enthusiasm for this groundbreaking project, and your academic transcripts.

Key Dates:

• Project Start: January 2024, with some flexibility.
• Application Deadline: 30th of November 2023. But don't wait; we're actively reviewing applications and scheduling interviews.

Project Duration:

• Earn 30 ECTS credits while contributing to the future of autonomous driving.

For Questions: Contact John Dahl at john.dahl@zenseact.com.

Ready to contribute to groundbreaking discoveries? Apply now and let your thesis be a testament to innovation at Zenseact! 🚀

More about Zenseact

Our software makes a difference.
Using AI-based technology to create the ultimate driver support, we’re fighting to end car accidents and make roads safe for everyone. Around 1,4 million people die in traffic yearly while approximately 50 million people get injured. Many get disabled as a result of their injury. We can do better.

One purpose, one product.
We’re a software company dedicated to revolutionizing car safety. By designing the complete software stack for autonomous driving and advanced driver-assistance systems, we’re fighting to end car accidents and make roads safe for everyone. Zenseact was founded by Volvo Cars, and the teams are based in Gothenburg, Sweden, and Shanghai, China.When we aim for zero accidents faster, we strive to speed up the transition to safe automation. This is essentially achieved by making cars updatable – like a computer or a phone. With regular software updates, a vehicle can be made safer long after its production. By accelerating improvement loops, shortening development cycles, and deploying high-capacity software quickly, we can make cars safer, faster.

Culture with people at heart
To achieve our mission of saving lives and ending traffic accidents is to go where nobody has before. It requires us to venture into the unknown, pioneering new technology and pushing the frontier of autonomous driving. While there’s no denying our determination and expertise, we must stand united to succeed. By fostering a culture of support and enablement – a place of psychological safety where all of us can thrive – everything else will follow. We call this a people-at-heart culture. This culture means caring. It means the company cares about me, and we care about one another. It means sharing, so we give each other energy and have fun together. Our culture is also about belonging. It’s important to feel at home and that we can be ourselves at work. Finally, a people-at-heart culture means well-being. So, we enjoy the flexibility needed to be and do our best – at work and in life.

Zenseact works proactively to create a culture of diversity and inclusion, where individual differences are appreciated and respected. To drive innovation we see diversity as an asset, which means we value and respect differences in gender, race, ethnicity, religion or other belief, disability, sexual orientation or age etc.
Interviews are held on a continuous basis, so we highly recommend that you submit your application at your earliest convenience.