What Alibaba’s Doing to Bring in the Future of Autonomous Driving

Compiled based on a presented by Wang Gang, representing the Autonomous Driving Laboratory of the Alibaba DAMO Academy and the Cainiao Network E.T. Logistics Laboratory.

Autonomous driving potentially could have a very large market size and social impact in the future, but there several technical difficulties and challenges that must be overcome before this can be made a reality.

Alibaba sees autonomous driving as being an important part of its logistics solutions in the future. In the process of realizing this vision, Alibaba is investing deeply in the research and development of this technology through a proprietary solution that leverages the computing capabilities of Alibaba Cloud and research at Alibaba DAMO Academy.

What Autonomous Driving Is and How It Works

What Does Autonomous Driving Actually Mean

  • Self-driving refers to any movement of a vehicle that is completed without the intervention of a human driver to control the vehicle.
  • Autonomous driving refers to a vehicle automatically performing control actions, such as steering, accelerating, and breaking, without a human driver’s direct input.
  • Intelligent driving refers to driving that is assisted by intelligent technologies, such as voice warnings or reminders.

There are a few conclusions we can draw from these definitions. First, the terms self-driving and autonomous driving do have some overlap, but where the two differ is that self-driving emphasizes the fact that there is no need for human control over the motor vehicle whatsoever, whereas autonomous driving may to some extent involve a human in some way or another, as we will discuss further below. Last, intelligent driving is in many ways an umbrella term that includes autonomous driving, self-driving, and other technology-assisted driving technologies. But, where autonomous driving and intelligent driving differ is that intelligent driving specifically refers to the use of technologies to assist or replace drivers at certain stages or the driving experience or to optimize aspects of the driving process.

In general, the term autonomous driving is the most representative of this field and the ultimate goal of related research. To build a better understanding of what exactly is autonomous driving, we can classify different levels of autonomy and what that means in terms of the necessary technology. To this end, the SAE proposed what is now the generally accepted classification scheme for the five levels of autonomous driving.

How Autonomous Driving Works

As shown in the figure above, autonomous driving works by creating an autonomous system that can realize all of the functions and responsibilities of a human driver. For instance, the driver’s environmental awareness can be transferred to sensors and perception algorithms, which effectively work as the driver’s eyes and ears. Sensors used for this application include cameras, GPS, millimeter wave radar, and laser sensor systems, or Lidar. Next, the driver’s mental attention in the driving experience, including the driver’s decision making and planning can be imitated with decision algorithms that is implemented on a specialized computing platform that usually will have a dedicated chip, such as an AI accelerator or neural processor. Last, the driver’s physical control over the vehicle by their hands and feet can be replicated through a system of a control algorithm that is connected to a physical wire control system. In summary these are the technical means by which autonomous driving can be implemented to take control over the mechanisms originally completed solely by a human driver.

Roadblocks in the Development of Autonomous Driving

Different Developmental Routes Adopted by Different Enterprises

One-step development, on the other hand, is mainly adopted by tech enterprises and start-ups. These enterprises do not have to worry about adapting existing products to the technology and have a strong drive to innovate and adopt cutting-edge technologies. This approach attempts to directly take a leading position in technology and grasp the core advantages of self-driving.

In other words, different enterprises choose different technical solutions for sensors and decision algorithms. This is due to the technological immaturity in subfields and the overall development strategies of enterprises in this industry varies greatly, with no clear standard. A mature technical development route has yet to emerge, with different technology routes developing concurrently. The radical or conservative routes chosen by different enterprises are always based on the advantages and existing strategies of these enterprises.

Complex Challenges Involved in Autonomous Driving Research

Part of this challenge is that driving naturally involves a large set of scenarios, behavior, and driving environments. For instance, driving environments include high-speed travel over highways, in-town driving, and driving in rural locations, as well as several specific settings such as roundabouts. Moreover, environmental factors that can also severely impact driving include weather events and time of day. All of these scenarios must be taken into account to successfully develop an autonomous driving system.

In other words, all of these factors, scenarios, as well as the several different behaviors that have to be included in the development of an autonomous driving system serve as some major roadblocks to developing more mature autonomous systems.

Alibaba’s Mission for an Autonomous Driving Future

Autonomous Driving Will Revolutionize the Logistics Industry

To realize this future, Alibaba has already begun large-scale efforts to implement preliminary research and development through its logistics distributions drivers on public roads throughout China. The data collected has allowed Alibaba to be able to build refined and high granularity data sets on a variety of specific driving scenarios.

Besides this, Alibaba Cloud has also experienced with unmanned logistic vehicles. Among these included a self-driving mailbox that has been test to much success at Shanghai Communications University in China.

How Alibaba’s Doing Autonomous Driving Research Differently

The AutoDrive Platform along with the Refined Scenario Database has given Alibaba an edge by lowering latency and improving the overall performance of perception and detection algorithms. Even though Autonomous driving still largely remains an area where several large and complex questions still remain. However, these strides points to several amazing possibilities in the imminent future.

The figure below provides a simple overview of the research and development processes at work. Data from Autonomous driving vehicles as well as data from data collection vehicles is feed into the scenario database at a massive level, well in the exabytes. Data is organized into specific scenario-based systems within the refined scenario database. Next, AutoDrive Platform, which is hosted on Alibaba Cloud, served as the hub for data tagging and management, autonomous driving evaluation, and efficient model training.


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