Autonomous driving vehicles may be more ubiquitous much sooner than we originally expected. The commercialization of self-driving technology is expected to gain momentum in China in the next few years, thanks to continuous technological innovation and considerable policy support, industry experts said. China has taken the lead in the research and development as well as application of autonomous driving technology around the world and it is the first country to allow fully driverless paid robotaxi operations, as the market potential of this technology continues to grow in the nation, they added. The self-driving industry is set to witness robust growth in the coming years. The market size of China's self-driving taxi services is expected to surpass 1.3 trillion yuan ($188.6 billion) by 2030, accounting for 60 percent of the country's ride-hailing market by then, said a report by global consultancy IHS Markit.

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency.

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.

Autonomous truck company Ike recently received an order of 1000 trucks from DHL, Ryder and NFI. Logistics companies hope that the automation software and sensors that Ike has developed will save lives, improve operating margins and keep drivers close to home, said Ike. eDelivery reported that this solution is designed for long-haul highway driving, and will rely on human drivers to navigate the more complex routes. The Hyunday and Aptiv venture Motional released a dataset expansion of over 1.4 Billion annotated lidar points, reported VentureBeat. The dataset called NuScenes now includes NuImages, an aggregation of 100 000 2D images that represent challenging driving conditions designed to boost safety for AVs in complex situations. Instead of building fully autonomous planes from the ground-up, Californian startup Xwing has started to unveil its Autoflight System targeting an aircraft agnostic approach.

It is only a matter of time before driverless cars take us to work and our children to school, according to James Peng, CEO and co-founder of Pony.ai, a California-based self-driving car start-up. "If I have to give a number, I'll say probably in five years," Peng told CNBC's Deirdre Bosa at the East Tech West conference in the Nansha district of Guangzhou, China. "We'll definitely see a wide adoption of autonomous driving vehicles -- fully autonomous driving vehicles -- on the open roads." That could happen in any part of the world, but Pony.ai has been focusing on the U.S. and China, where the start-up has been testing autonomous vehicles. It recently partnered with Hyundai to introduce an on-demand vehicle service for residents in Irvine, California, where passengers can share autonomous cabs using an app.

In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at https://github.com/ApolloAuto/apollo/tree/master/modules/planning.

Autonomous driving systems are changing the way we think about the future of personal transportation. How soon will we have access to vehicles that don't require human control? Are driverless cars just around the corner? What will our travel be like if we're spending a lot less time behind the wheel? What technology actually makes autonomous driving possible?

Everyone in business wants to know what's going to happen in the future, and for some time now Ford has been investing in futurism, an evolving academic and professional discipline. The need for this was particularly evident after the Business Insider Transportation team in New York spent a few days at the New York Auto Show, asking everyone to predict was will happen in 2016 -- and beyond. The car business these days is all about change: automakers becoming "mobility providers," electric cars potentially displacing gas-powered vehicles, even autos driving themselves. Heck, even Apple may get in on the action. For nearly a decade, Sheryl Connelly has been Ford's manager of global consumer trends and futuring.