What are the flaws inherent in the HD-mapping approach to autonomous driving?
📉 Expensive Creation and Maintenance
HD maps are expensive. Creating detailed, high-definition maps of an entire city or region is a time-consuming and resource-intensive process. Specialized mapping companies often spend years gathering and processing data to create these maps, and maintaining their accuracy becomes an ongoing challenge. The discrepancy between HD maps and real-world conditions mandates costly updates and constant maintenance to keep these maps relevant. These creation and maintenance costs, passed on to the OEM or Tier-1, are prohibitive.
🕐 Lack of Real-Time Information
A significant drawback of HD maps is that they do not represent real-time conditions on the road. Autonomous vehicles need to be aware of dynamic situations, such as detours, road closures, construction, various objects on the road, traffic accidents, and more. HD maps cannot provide this real-time information, leading to potential issues when autonomous vehicles encounter unexpected roadblocks or obstacles. The mismatch between HD maps and the current state of the road can lead to confusion and challenges in decision-making for the self-driving vehicle, creating new hazards (much to the frustration of human drivers who share the road).
🤝 Dependency on Vendors
Relying on HD maps can create a dependency on the vendors who produce and maintain them. There are only a few companies attempting to map the entire world, and their efforts have been met with limited success. This dependence on third-party entities can limit the flexibility and adaptability of autonomous driving systems.
💽 Data Storage Challenges
Storing HD maps locally on autonomous vehicles poses a variety of challenges. These maps can be incredibly large in size, making it impractical to download them over the air and store them on autonomous vehicles. For example, downloading a high-definition map of a large city like Los Angeles or New York would require several terabytes of storage, which is not economical for most vehicles.
📡 Communication Limitations
While cloud communication is a potential solution to avoid storing large HD maps locally, it is not always a reliable option. Continuous high bandwidth is required for seamless communication, and this can create technical challenges, especially in areas with limited bandwidth coverage, network congestion, or tunnels. It is also quite expensive to maintain, and it is not reasonable to expect that these ongoing charges would be borne by vehicle owners!
Cyber Vulnerability
An autonomous vehicle using HD maps for navigation gets its driving directions from the outside via an open communication port. Therefore, it is prone to cyber-attacks.
🚜 Irrelevance for Off-Road Applications
HD maps are primarily designed for regular public roads in urban, suburban, and highway environments. But these HD maps are completely irrelevant for off-road applications, such as mining or agriculture, where the terrain and conditions are vastly different. In these scenarios, the road infrastructure does not exist as it does in city settings, rendering HD maps useless.
📍 Localization and Perception Challenges
Using HD maps introduces the challenge of localization, where autonomous vehicles need to determine their exact position on the map. GPS signals in cities can be unreliable due to a variety of effects like “GPS canyon” and underground passage, to name a few, whereas using a compass for heading is not always accurate. Reconciling the differences between the HD map’s predictions and the actual environment requires a perception system, which can again lead to uncertainty and decision-making dilemmas (e.g., Which system to believe? The HD map or the perception layer?).