Prototyping of level 3 autonomous vehicles on separated roadway lanes. Among the first open-road experiments in France.Our team was in charge of all non-control software aspects: data capture and fusion, decision-making, display. Very ambitious guidelines guided our design, with the goal of achieving a system:
A continuation of the Highway Chauffeur project.
SAM (Safety and Acceptability of autonomous driving and Mobility) project brings together a consortium of actors in response to the Autonomous Road Vehicle Experimentation (EVRA) call for projects launched by the agency for the environment and energy management (ADEME).
The work involves prototyping level 3 and 4 autonomous shuttles between business areas (mainly highways and some semi-urban areas around arrival zones) on the La Defense / Roissy-Charles-de-Gaulle and Massy / Dourdan routes.
Recording data during open road experiments and the ability to analyze/play them back is a critical issue in the design of any autonomous prototype.
We are developing a tool that addresses this issue in a generic enough way to be used on all prototypes, whether they are trains or cars. It diagnoses the present/absent sensors, robustly records received data, and makes them available in shared memory for real-time exploitation by third-party software, which in turn can produce data to include in the recording.
A software library also allows playback of the recorded data.
A robust and independent application to diagnose, collect, and provide real-time data from all types of sensors.
Recording is good. Replaying is better!
Good understanding of the road is necessary for anticipating the intentions of other road users. Sensors do not provide sufficiently accurate information or at a sufficiently long distance, unlike HD Mapping.
The use of real-time centimeter-precision HD Mapping required the development of a lightweight mapping format, efficient processing functions, display shaders, as well as conversion/generation/manual input/correction tools.
Our mapping tools are generic enough to be used for both autonomous cars and trains, as well as in our urban simulations.
Our mapping is based on HERE HD LiveMap, which is automatically converted into our format:
Unavailable or non-updated map portions can be created or corrected using our editor.
Positioning in HD mapping relies on multiple sources (GPS, camera lines, inertial sensors, etc.). The challenge is to produce smooth positioning (comfortable trajectory tracking) while remaining faithful to the sensors and robust to potential malfunctions (for example: loss of GPS signal when passing under a bridge, erased lines on the road, etc.).
The use of lines captured by the camera allows to compensate for the imprecisions of GPS or map data.
The level of confidence that can reasonably be given to a sensor can suddenly vary depending on the environment. SpirOps is therefore developing a system:
An accurate reconstruction of the environment is essential for an autonomous vehicle to make the right decisions. Since sensors have different properties, none of them provides an absolute truth and our job is to make the most of each sensor.
This involves analyzing the strengths and weaknesses of different technologies, filtering out their flaws, implementing inter-sensor redundancy when necessary, and ensuring the physical consistency of reconstructed vehicles and their maintenance when they go out of perception zones or are masked by other objects.
The requirements of the project led us not to use the high level data provided by each sensor but rather to exploit the lowest level data possible in order to minimize over-aggressive filtering or imperfect tracking, and to make arbitrations as late as possible, thus exploiting the maximum amount of information (better understanding of the road situation, vehicle maneuvers, concordances/divergences between sensors).
Maximizing the use of each sensor to track:
Anticipating driving decisions can save precious milliseconds:
Autonomy comes from the ability to make decisions, that is, to arbitrate between several possible choices, between several conflicting objectives. When driving, constant trade-offs are necessary between comfort, speed, itinerary compliance, politeness, while ensuring a solid foundation of respect for the rules of the road.
In the field of autonomous vehicles, it is particularly important to be able to explain these trade-offs and to offer guarantees on the areas of operation of the decision-making systems.
Our patented decision-making technology is perfectly suited for creating a driver AI.
Ensuring that all the components mentioned above maintain their quality, even when significant modifications are made, is a very important challenge to ensure that the project progresses in the right direction. We develop tools that allow us to check, for each change, the performance on each of the above themes and to easily reproduce any regressions discovered.
Ground truth editor for non-regression tests.
To prevent regressions, we have for each commit:
Assists in prototyping new behaviors.