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Wir rekrutieren kluge K\u00f6pfe und setzen uns f\u00fcr eine Kultur der gegenseitigen Unterst\u00fctzung und Inklusion ein. So k\u00f6nnen mit Ihren Ideen zu weltver\u00e4ndernden Innovationen und bahnbrechenden Technologien beitragen. Damit das m\u00f6glich ist, nutzen wir die ganze Breite und Tiefe unseres weltweiten Know-hows. Wir liefern Antworten, machen Komplexes einfach und \u00fcberwinden gemeinsam anspruchsvollste Herausforderungen.</p> <p><strong>Innovationen mit Technologieexperten<br /></strong>Karriere kennt keine Grenzen \u2013 davon sind wir bei Qualcomm fest \u00fcberzeugt. Nutzen Sie die einzigartige M\u00f6glichkeit, mit hoch angesehenen und f\u00fchrenden Engineering- und Technologieexperten der Branche zusammenzuarbeiten. Bei uns erleben Sie Lernen und berufliche Weiterentwicklung in einer v\u00f6llig neuen Dimension.</p> <p><strong>Gesund leben, gesund arbeiten<br /></strong>Dar\u00fcber hinaus haben Sie Zugang zu Programmen wie unseren kontinuierlichen Lern- und Weiterentwicklungsprogrammen, der Erstattung von Studiengeb\u00fchren und Mentoring, um Ihr grenzenloses Potenzial auszusch\u00f6pfen \u2013 au\u00dferdem haben Sie die M\u00f6glichkeit, Ihre Lebensqualit\u00e4t durch unsere umfassenden, erstklassigen Leistungsangebote zu verbessern .</p> <p>Die Arbeit, die wir bei Qualcomm leisten, hat Auswirkungen auf das Leben rund um den Globus \u2013 und Sie k\u00f6nnen ein Teil davon sein. Bewerben Sie sich noch heute und entfalten Sie Ihr volles Potenzial..</p>"}}}, "customContent": {"positionSections": [{"title": "Unlock Your Limitless Potential with Qualcomm", "body": "<p>Whether you&rsquo;re launching a new career or ready to explore what&rsquo;s next in the evolution of your talent and expertise, you&rsquo;re about to embark on a career growth journey like no other.</p> <p><strong>Bring out your best, with the best<br /></strong>Our employees make Qualcomm&rsquo;s success possible. We hire the brightest minds and foster a supportive, inclusive culture where your ideas have the power to contribute to world-changing innovations and breakthrough technologies. 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Learn more about how our culture of collaboration and robust benefits program allow our employees to live well and exceed their potential.", "recaptcha_enabled": 0, "companyName": "Qualcomm", "showLoggedOutNotificationsPrivacyPolicy": true, "hideEightfoldBranding": false}, "pid": "446702564463", "positions": [{"id": 446702564463, "name": "FY25 Intern - Thesis Safety-Aware Motion Planning - G\u00f6teborg", "location": "Gothenburg, Sweden", "locations": ["Gothenburg, Sweden"], "hot": 0, "department": "Interim Intern", "business_unit": "38010 QCT Auto AD Cust Func AS SWE", "t_update": 1730937600, "t_create": 1730937600, "ats_job_id": "3067459", "display_job_id": "3067459", "type": "ATS", "id_locale": "3067459-en", "job_description": "Company: Arriver Software AB Job Area: Interns Group, Interns Group > Interim Intern General Summary: Field: Collision Avoidance feature development Background: Collision Avoidance Systems are designed to prevent or reduce the severity of a collision. This can be done by automated braking and/or steering if a threat is detected. Predicting the threat of a collision with road users and their future position is a key part of Collision Avoidance Systems, to know when an action is necessary to avoid an incident. Currently, threat estimation is based on object detection and motion predictions. In real-world, object detections can be uncertain due to sensor issues, occlusion, or unpredicted motion behavior. This could lead to additional risks in scenarios where human drivers would react and take precautionary actions. For example, in a case where pedestrians might be occluded by a stopped bus or might arrive at a crosswalk. The Collision Avoidance team is, with this thesis, interested in exploring how to estimate risks in future traffic situations and how precautionary actions can be planned to mitigate the risks. Purpose: This thesis investigates how to detect situations that pose an increased risk and define the best longitudinal and lateral paths ahead within a fixed horizon associated with the risks. This information can then be used by the planning system or the driver to take precautionary action to mitigate the risk. The main input for the functionality will be perception data in the form of objects originating from fusion of computer vision and radar, as well as road geometry detection from computer vision. Additional data such as object prediction, road signs, ego motion, and map data can be utilized. Ego vehicle's longitudinal and lateral path that has the lowest cost function is identified by evaluating all longitudinal actions (like acceleration and deceleration) as well as lateral maneuvers (such as staying in the current lane or changing to the left or right lane). The cost function should measure the collision risk within the given time. This involves considering the detected objects within this timeframe, including prediction, and assessing the probability of their movements, even those that are occluded. Using the predicted stochastic environment and an ego vehicle model, cost function could be evaluated within the horizon. The main output will be limitation in allowed driving speed and lateral motion by designing a controller (steering, braking, warning). The thesis will contain the following: - Perform literature study on which methods are suitable to solve the problem. - Define traffic scenarios that are most relevant for the analysis. - Implement the selected strategy. - Evaluate how well the method can detect hazardous situations, from simulated and real-world data. - Evaluate if possible precautionary driving limits can reduce future risks if followed. Learning objectives: Throughout the work the student(s) will learn how to apply theoretical knowledge on a real-world problem and get a broader understanding of the challenges faced when developing ADAS features today. References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies. Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. If you would like more information about this role, please contact Qualcomm Careers. Field: Collision Avoidance feature development Background: Collision Avoidance Systems are designed to prevent or reduce the severity of a collision. This can be done by automated braking and/or steering if a threat is detected. Predicting the threat of a collision with road users and their future position is a key part of Collision Avoidance Systems, to know when an action is necessary to avoid an incident. Currently, threat estimation is based on object detection and motion predictions. In real-world, object detections can be uncertain due to sensor issues, occlusion, or unpredicted motion behavior. This could lead to additional risks in scenarios where human drivers would react and take precautionary actions. For example, in a case where pedestrians might be occluded by a stopped bus or might arrive at a crosswalk. The Collision Avoidance team is, with this thesis, interested in exploring how to estimate risks in future traffic situations and how precautionary actions can be planned to mitigate the risks. Purpose: This thesis investigates how to detect situations that pose an increased risk and define the best longitudinal and lateral paths ahead within a fixed horizon associated with the risks. This information can then be used by the planning system or the driver to take precautionary action to mitigate the risk. The main input for the functionality will be perception data in the form of objects originating from fusion of computer vision and radar, as well as road geometry detection from computer vision. Additional data such as object prediction, road signs, ego motion, and map data can be utilized. Ego vehicle's longitudinal and lateral path that has the lowest cost function is identified by evaluating all longitudinal actions (like acceleration and deceleration) as well as lateral maneuvers (such as staying in the current lane or changing to the left or right lane). The cost function should measure the collision risk within the given time. This involves considering the detected objects within this timeframe, including prediction, and assessing the probability of their movements, even those that are occluded. Using the predicted stochastic environment and an ego vehicle model, cost function could be evaluated within the horizon. The main output will be limitation in allowed driving speed and lateral motion by designing a controller (steering, braking, warning). The thesis will contain the following: - Perform literature study on which methods are suitable to solve the problem. - Define traffic scenarios that are most relevant for the analysis. - Implement the selected strategy. - Evaluate how well the method can detect hazardous situations, from simulated and real-world data. - Evaluate if possible precautionary driving limits can reduce future risks if followed. 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