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| Autonomy is part of the next big megatrend in electronics which is likely to change society. As a new technology, there are a large number of open research problems. These problems can be classified in four broad categories: Autonomy hardware, Autonomy Software, Autonomy Ecosystem, and Autonomy Business models. In terms of hardware, autonomy consists of a mobility component (increasingly becoming electric), sensors, and computation. | Autonomy is part of the next big megatrend in electronics which is likely to change society. As a new technology, there are a large number of open research problems. These problems can be classified in four broad categories: Autonomy hardware, Autonomy Software, Autonomy Ecosystem, and Autonomy Business models. In terms of hardware, autonomy consists of a mobility component (increasingly becoming electric), sensors, and computation. |
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| Human-Machine Interface (HMI) for autonomy remains an area with several open research and design challenges, particularly around trust, control, and situational awareness. One major issue is how to build "appropriate trust and transparency" between users and autonomous systems. Current interfaces often fail to clearly convey the vehicle’s capabilities, limitations, or decision-making rationale, which can lead to overreliance or confusion. There's a delicate balance between providing sufficient information to promote understanding and avoiding cognitive overload. Additionally, ensuring "safe and intuitive transitions of control," especially in Level 3 and Level 4 autonomy, remains a critical concern. Drivers may take several seconds to re-engage during a takeover request, and the timing, modality, and clarity of such prompts are not yet standardized or optimized across systems. Another set of challenges lies in maintaining "situational awareness" and designing "adaptive, accessible interfaces." Passive users in autonomous systems tend to disengage, losing track of the environment, which can be dangerous during unexpected events. Effective HMI must offer context-sensitive feedback using visual, auditory, or haptic cues while adapting to the user’s state, experience level, and accessibility needs. Moreover, autonomous vehicles currently lack effective ways to interact with external actors—such as pedestrians or other drivers—replacing human cues like eye contact or gestures. Developing standardized, interpretable external HMIs, a language of driving, remains an active area of research. Finally, a lack of unified metrics and regulatory standards for evaluating HMI effectiveness further complicates design validation, making it difficult to compare systems or ensure safety across manufacturers. | Human-Machine Interface (HMI) for autonomy remains an area with several open research and design challenges, particularly around trust, control, and situational awareness. One major issue is how to build "appropriate trust and transparency" between users and autonomous systems. Current interfaces often fail to clearly convey the vehicle’s capabilities, limitations, or decision-making rationale, which can lead to overreliance or confusion. There's a delicate balance between providing sufficient information to promote understanding and avoiding cognitive overload. Additionally, ensuring "safe and intuitive transitions of control," especially in Level 3 and Level 4 autonomy, remains a critical concern. Drivers may take several seconds to re-engage during a takeover request, and the timing, modality, and clarity of such prompts are not yet standardized or optimized across systems. Another set of challenges lies in maintaining "situational awareness" and designing "adaptive, accessible interfaces." Passive users in autonomous systems tend to disengage, losing track of the environment, which can be dangerous during unexpected events. Effective HMI must offer context-sensitive feedback using visual, auditory, or haptic cues while adapting to the user’s state, experience level, and accessibility needs. Moreover, autonomous vehicles currently lack effective ways to interact with external actors—such as pedestrians or other drivers—replacing human cues like eye contact or gestures. Developing standardized, interpretable external HMIs, a language of driving, remains an active area of research. Finally, a lack of unified metrics and regulatory standards for evaluating HMI effectiveness further complicates design validation, making it difficult to compare systems or ensure safety across manufacturers. |
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| | Finally, autonomy will have implications on topics such as civil infrastructure guidance, field maintenance, interaction with emergency services, interaction with disabled and young riders, insurance markets, and most importantly the legal profession. There are many research issues underlying all of these topics. |
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| | In terms of business models, use models and their implications for supply chain are open research problems. For example, for the supply chain, the critical technology is semiconductors which is highly sensitive to very high volume. For example, the largest market in mobility, the auto industry, is approx. 10% of semiconductor volume, and the other forms (airborne, marine, space) are orders-of-magnitude lower. From a supply chain point perspective, a small number of skews which service a large market are ideal. The research problem is: What should be the nature of these very scalable components. In terms of end-markets, autonomy in traditional transportation is likely to lead to a reduction in unit volume. Why? With autonomy, one can get much higher utilization (vs the < 5% in today's automobiles). However, it is also likely that autonomy unleashes a broad class of solutions in markets such as agriculture, warehouses, distribution, delivery, and more. Micromobility applications in particular offer some interesting options for very high volumes. The exact nature of the applications is an open research problem. |
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