Here is my list ,
some of the most known providers –

1. HERE
https://www.here.com/platform/adas-had
2. NVIDIA HD MAPS
https://www.nvidia.com/en-us/self-driving-cars/hd-mapping/
3. TOMTOM
https://www.tomtom.com/products/hd-map/
4. Mapbox
https://www.mapbox.com/
5. DeepMap
https://www.deepmap.ai/
6. Civil Maps
https://civilmaps.com/

It’s possible for driverless cars to greatly reduce traffic jams as they will affect them in multiple ways:

Lane switching
For routes that see assymetrical traffic flows at different times of the day, self-driving cars can choose how many lanes will go in each direction without a physical barrier between the directions. This also accomodates unusual traffic flows, like for special events. Exclusive reduction.
Uncontrolled intersections. If cars communicate with each other and the infrastructure, then traffic lights can go away. Cars will adjust their speed and position to flow through intersections without stopping. Exclusive.

Accident reduction
Because self-driving cars don’t fail due to human error, they will eliminate a large portion of accidents. Proportional reduction.
Reduction of rubbernecking traffic jams. When passing an accident or even just an unusual sight, most drivers slow down to get a better look at it. Self-driving cars won’t do this. Network reduction.

Load balancing
Cars can check the traffic on all available routes and choose the path with the least traffic. This ensures that many roads will be used equally. This will reduce traffic on the worst routes. Immediate reduction for user, network reduction for everybody else.
Space reduction. Self-driving cars can potentially reduce the space between adjacent cars. This will allow greater density on the roads. Once all cars are self driving, the side-to side distance can be reduced as well. This works because intercar communications and sensors provide a much faster reaction time. Network/exclusive effect.

Damping of ghost slowdowns
When one car brakes suddenly, cars further back will brake. Thic creates a wave that goes back and can reportedly last for hours. Self-driving cars that communicate with each other can account for this and damp the effect. Network reduction.
Hazard communication. Cars can communicate about up coming road hazards to cars behind them. This will allow cars to move over in time to avoid the hazard. Network reduction.

The control software first builds a model of the surrounding. This means it uses the pictures from the cameras and the data from radar and lidar to derive the location, the velocity and the acceleration of the own car and all the other cars, bicycles, pedestrians, animals, socceer balls etc. on the road. This model is then used to predict the movement of all traffic participants on the road.

Since the acceleration and deceleration is limited by physical boundaries (i.e. how slippery is the raod), the possible outcomes of the next seconds can be predicted taking different reactions of the participants into account. The bad situation is, if the outcome of the prediction is a crash independent of possible reactions of the participants. Then the control software tries to limit the damage. The strategy for the limitation is a point of big debate. Who will be favoured by the strategy? The driver or the other drivers or an average solution?

There are also simpler versions of control software available, which are strongly based on neural networks for the derivation of the control commands (stearing and acceleration/deceleration). These simpler versions are currently not built to predict accidents. They just try to avoid contact with other traffic participants.