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Sparrho Insights: 3-minute summaries of cutting-edge science based on peer-reviewed research

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Self-driving cars: When will it really happen? Further reading for 16 Mar 2017 issue

In 10 seconds? Detecting and protecting pedestrians is still a big challenge for fully autonomous cars and assisted driving systems. How would you feel about riding a vehicle that might sacrifice you to minimise fatalities in an emergency situation?

Not convinced? Progress in machine learning method has increased the accuracy of pedestrian detection by leaps and bounds (see below + read more), but the question of ethics behind the driving systems remains unresolved.

What's the dilemma?

Who to kill – Imagine the dilemma during an emergency: should a driverless car swerve out of the way of pedestrians at the expense of driving into a concrete wall and killing its passengers? Manufacturers of self-driving cars are in essence predetermining the survival of their riders through programming decisions they make now. (read more)

Nobody wants to say – Mercedes Benz is the only company so far to have made a public statement, but quickly reversed their position after a manager's interview was sensationalised into headlines like Mercedes's Self-Driving Cars Will Kill Pedestrians Over Drivers.

Given all this talk of ethics, when will fully autonomous cars hit the road?

Singapore is pioneering – In August 2016, MIT software spinout nuTonomy partnered with ride-hail platform Grab to launch the world’s first autonomous taxis in Singapore, with the aim to create a full fleet by 2018. (read more)

So a taxi with no human at all? – Well not yet, in this current test phase, a human engineer still sits in the front seat on every ride, prepared to take the wheel if necessary, while a backseat researcher monitors the vehicle's computers. (read more)

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17 ITEMS PINNED

Effective pedestrian detection using deformable part model based on human model

Abstract: Abstract Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.AbstractRecently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

Pub.: 25 Oct '16, Pinned: 11 Mar '17

Product-to-Service Extension: The Impact of Brand Equity on Upscaled Service

Abstract: A recent trend in the luxury industry shows many companies using brand extension strategies to leverage their assets among competitors. Despite the popularity of brand extensions, limited research has been conducted to determine its effectiveness when using parent product brands in order to introduce new service brands. Thus, the primary purpose of this study is to propose the framework to increase the understanding of luxury brand extensions by focusing on the horizontal category brand extension from product to service. The impact of perceived quality, brand association and brand loyalty of the luxury car brand on consumers’ perception and attitude toward the rental car service brand was observed to differentiate from other research that focuses on brand extension within product categories. A total of 324 samples were collected and analyzed using structural equation modeling with AMOS. The brand association of the parent brand showed a significant impact on the evaluation of the extended service brand. In addition, a high level of the brand association was also found to influence the final purchase decision of the extended service brand. However, this study could not find any significant effect of perceived quality and brand loyalty for the parent product brand on the extended service brand. The framework proposed in this study has merit to increase the understanding of service brand extensions by exploring luxury car brands and luxury rental car brands. In the service brand extension, there was a gap in consumer perception between the product brand and the extended service brand. In order to maximize the positive impact of the parent brand, marketers and retailers should investigate the different roles of the brand equity items.

Pub.: 25 Jul '16, Pinned: 16 Mar '17

Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study.

Abstract: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting.With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions.Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations.We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system.Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments.In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.

Pub.: 23 Dec '16, Pinned: 07 Mar '17