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CURATOR
A pinboard by
George Ng

I have a Doctorate in Biotechnology and I'm a machine learning expert based in Hong Kong

PINBOARD SUMMARY

Singapore startup, nuTonomy has partnered with Grab Taxi to begin trials.

That will usher in the world’s first autonomous taxis. But a minor accident with a lorry put a bumper in its plans.

How Safe Are Self-driving cars? Telsa's computer vision-based vehicle detection system combined with GPS determines the car’s position on the road, warning drivers of an impending collision when objects stray into their path. But Joshua Brown, 40, of Canton, Ohio, was killed driving a Tesla Model S with the 'self-drive' feature switched on when a trailer turned left into his path. The “technical failure” of the automatic braking system happened because the collision-avoidance system kicks in, only when radar and computer vision systems agree an obstacle lies ahead.

To help cars become really autonomous

The Nevada Center for Advanced Mobility is building smart road infrastructure that allows for the vehicle-to-infrastructure and vehicle-to-vehicle communications. The partnerships between the state, private and academic entities, helps Nevada track vehicles and what they see while the state concentrates on the infrastructure. The information gathered from this pilot program render cars into rolling 'data centres' eager to utilise and distribute data.

What Opportunities Will Smart Cities Offer

For Tech Giants like Apple - who outlined plans to develop self-driving technology. Cook, however, was reluctant to reveal whether Apple will eventually produce its own self-driving car. Choosing instead to invest $1 billion last year in Didi Chuxing, the biggest Chinese ride-hailing service.

For Entreprenuers like Elon Musk - who revealed plans to bore a series of tunnels under major cities. Work on its first tunnel beneath Los Angeles, which will run from Los Angeles to Sherman Oaks has begun. Inside these tunnels, cars will sit on pods that travel 200 kilometres per hour, going from Westwood to Los Angeles in 5-6 minutes.

For Cities like Delft - who built a 30-meter long test facility which can support tests on a full-scale Hyperloop pod at low speeds. Hyperloops are a driverless high-speed transportation system where pressurised passenger passenger cabins, travel at speeds of 600+ mph driven by linear induction motors and air compressors. Due to its large urban populations, the Netherlands is the most logical place to install the Hyperloop.

17 ITEMS PINNED

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: 15 Jun '17

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: 15 Jun '17