Recently, a Tesla in Utah ran into the back of a stationary fire truck at high speed. This is the second such incident this year and the National Transportation Safety Board is already investigating the earlier incident. Incidents involving Teslas get news coverage because of the strident safety claims made by Elon Musk for his company’s AutoPilot driver assist system, but such accidents can happen with many vehicle brands. Relying on a single sensor for active safety control is often inadequate, but high definition (HD) maps may actually turn out to be part of the solution.
Teslas, and many millions of other vehicles, are equipped with forward-looking radar sensors that are used for adaptive cruise control (ACC). The radar is used to detect a vehicle moving ahead while ACC is active and measures the gap to that vehicle. If the lead vehicle slows down, the ACC vehicle will automatically slow to maintain a safe gap.
Forward-Looking Sensors Not Seeing Everything
You might think that if ACC detects a stopped vehicle it would automatically slow to a stop, but as the two recent crashes indicate, this isn’t always true. When ACC is used at highway speed, the assumption is that the other vehicles on the road will also be moving. To prevent false positives that would cause the brakes to erroneously engage, these systems are designed to ignore static objects like road signs, light poles, etc.
When another static vehicle that was outside of the radar range comes within view of the sensor while moving at highway speeds (as both vehicles in these crashes were), it is not assumed to be a vehicle and thus it is ignored. Some vehicles also include a combination of automatic emergency braking and/or forward collision warning safety systems to prevent crashes, but these systems are not optimized for identifying stationary vehicles in the roadway when the vehicles are traveling at highway speeds. Refinements in the coordination between these systems will continue.
How Does Mapping Fit into This?
Today, increasingly detailed maps are being used not just for routing but also as inputs to hybrid propulsion systems and long-range sensors in partially automated vehicles from GM and Mercedes-Benz. In the coming years, HD maps with detailed locations of static objects will be used for precision localization. If a vehicle has HD maps with the locations of fixed roadside objects, it may be possible to fuse this with the real-time radar data to better understand which objects can safely be ignored. The addition of image data from the camera used for lane keeping assist and it should be possible to recognize legitimately stopped vehicles and respond accordingly.
Companies such as San Francisco startup Mapper and incumbent map providers like HERE and TomTom have begun building HD maps. Mapper has developed a low cost, multi-camera-based data collection system that can be installed in vehicles used for ride-hailing providers or in other fleets. By the end of 2018, up to 2 million vehicles from Volkswagen, BMW, and Nissan are expected to be on the road globally with Mobileye’s latest EyeQ4 image processor. These vehicles will also be collecting data that feeds into Mobileye’s Road Experience Management system and then into maps from providers including HERE.
The sooner we start augmenting existing driver assist systems with new data sources such as HD maps or fusion of other sensors in the vehicle, the sooner object classification should improve to help prevent more crashes. The Tesla crashes are getting the attention, but these are problems that afflict virtually every manufacturer and the technology needs to be improved in order to save more lives.
Tags: Automated Driving Systems, Automated Vehicles, Automotive Mapping, Transportation Efficiencies
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