Thinking Wave

Thinking Wave

Exploration of increasing drivers trust in a semi-autonomous vehicle through real-time visualizations of collaborative driving dynamic As we progress towards a world of fully autonomous vehicles, designing semi-autonomous solutions will be essential in addressing road and behavioural safety. In particular, designing and calibrating trust will be a key part of delivering a safe experience for all stakeholders. The Thinking Wave is an ongoing exploration on visualising the capabilities of vehicle system so that drivers can understand when and how they should take over the wheel. Over-trusting a vehicle system’s capacity to drive and handle situations can lead to hazardous accidents but the same result could occur by under-trusting. Under-trusting AVs in simple scenarios and forcefully taking the wheel could increase risk of accidents. Human error may become a factor in when otherwise mitigated. In conjunction with mobility research teams at the University of Tokyo, we are building the Thinking Wave as a testable prototype. At present, DLX is exploring two visualisation designs. Concept one, called “tidal” is a wave which indicates the level of difficulty the vehicle is encountering, suggesting the driver to adjust control or takeover. It fluctuates in intensity (amplitude, frequency and speed) to reflect the effort of the vehicle system. We took inspiration from existing and familiar visualisations of human ECGs as such biometric data is intuitive. In the same way humans under stressful conditions such as heart-rate, the vehicle would reflect intensity with multiple waves close to one another of high amplitud e when going through a difficult handle-situation. For example, in the instance of driving through heavy rain, visibility is lowered and the vehicle’s sensors may be compromised in detecting its surroundings. As the system would struggle to adequately handle the situation, the wave would appear high, frequent and fast. Concept two, called “tandem” is a multi-information layer visualisation. It focuses on showing the collaboration dynamic between vehicle and driver on top of also showing the vehicle’s effort throughout various scenarios. A circle, symbolising the driver and a square symbolising the vehicle system changes in size, position and rotation speed to reflect this. The leading controller within a scenario would be on the top (front) of the visual. The larger/smaller each shape shows which of the two is putting more/less effort into handling the scenario respectively. As for the vehicle system’s thinking effort, the more complex the situation (regardless of which was primary or dominant), the faster it spins. The Thinking Wave designs show potential in helping drivers not only calibrate their expectations but also establish trust in AVs. We also believe that it can be an effective tool which evolves both with the technology and driver to establish a safer and more effective AV future. If the Thinking Wave can learn driver’s handling tendencies, it could help the vehicle system adapt and control in a more preferable manner to the driver. Furthermore, with software updates and enhancements in autonomous systems, the wave’s confidence visualisation can be altered to help drivers recalibrate their trust. This concept demonstrates potential in educating drivers while giving way for innovation to still be enabled and adequate to the designs. It could even be said that because of this, it is a suitable and promising concept that continues to evolve beyond near-future autonomous innovations. https://vimeo.com/541975345?embedded=true&source=video_title&owner=64801719

Exploration of increasing drivers trust in a semi-autonomous vehicle through real-time visualizations of collaborative driving dynamic As we progress towards a world of fully autonomous vehicles, designing semi-autonomous solutions will be essential in addressing road and behavioural safety. In particular, designing and calibrating trust will be a key part of delivering a safe experience for all stakeholders. The Thinking Wave is an ongoing exploration on visualising the capabilities of vehicle system so that drivers can understand when and how they should take over the wheel. Over-trusting a vehicle system’s capacity to drive and handle situations can lead to hazardous accidents but the same result could occur by under-trusting. Under-trusting AVs in simple scenarios and forcefully taking the wheel could increase risk of accidents. Human error may become a factor in when otherwise mitigated. In conjunction with mobility research teams at the University of Tokyo, we are building the Thinking Wave as a testable prototype. At present, DLX is exploring two visualisation designs. Concept one, called “tidal” is a wave which indicates the level of difficulty the vehicle is encountering, suggesting the driver to adjust control or takeover. It fluctuates in intensity (amplitude, frequency and speed) to reflect the effort of the vehicle system. We took inspiration from existing and familiar visualisations of human ECGs as such biometric data is intuitive. In the same way humans under stressful conditions such as heart-rate, the vehicle would reflect intensity with multiple waves close to one another of high amplitud e when going through a difficult handle-situation. For example, in the instance of driving through heavy rain, visibility is lowered and the vehicle’s sensors may be compromised in detecting its surroundings. As the system would struggle to adequately handle the situation, the wave would appear high, frequent and fast. Concept two, called “tandem” is a multi-information layer visualisation. It focuses on showing the collaboration dynamic between vehicle and driver on top of also showing the vehicle’s effort throughout various scenarios. A circle, symbolising the driver and a square symbolising the vehicle system changes in size, position and rotation speed to reflect this. The leading controller within a scenario would be on the top (front) of the visual. The larger/smaller each shape shows which of the two is putting more/less effort into handling the scenario respectively. As for the vehicle system’s thinking effort, the more complex the situation (regardless of which was primary or dominant), the faster it spins. The Thinking Wave designs show potential in helping drivers not only calibrate their expectations but also establish trust in AVs. We also believe that it can be an effective tool which evolves both with the technology and driver to establish a safer and more effective AV future. If the Thinking Wave can learn driver’s handling tendencies, it could help the vehicle system adapt and control in a more preferable manner to the driver. Furthermore, with software updates and enhancements in autonomous systems, the wave’s confidence visualisation can be altered to help drivers recalibrate their trust. This concept demonstrates potential in educating drivers while giving way for innovation to still be enabled and adequate to the designs. It could even be said that because of this, it is a suitable and promising concept that continues to evolve beyond near-future autonomous innovations. https://vimeo.com/541975345?embedded=true&source=video_title&owner=64801719