Risk Analysis

S No.DescriptionTypeLikelihoodConsequenceRisk Management Evaluation
Slow model execution during simulationT24Success: Only bottleneck is data throughput of the
CARLA simulator. Our system theoretically can
provide 100 FPS.
Performance of learning
model is not adequate
Fallback rule based model was implemented.
Inability to formulate
the modelling problem
S,M22Success: Since we had already prototyped a model
early in the spring, we managed to avoid this risk
4. Missing project milestonesS, M25Success:
- We were able to achieve all the required requirements.
- We were not able to achieve all the desired requirements
5Stakeholder disengagement M32Success: We always had regular meetings with our sponsors
and discussed all findings with them and we received
full and continued support from them throughout
6Inadvertent failure during demosS, M24Success: Our risk reduction strategy was successful here and
we did not face this risk during the demos.
7Unable to capture data due to
COVID-19 pandemic extension
S, M42Success: We were able to capture data outdoors as
the COVID situation was under control at the time
8Insufficient compute power
for training models
T44Success: We procured a powerful machine early on in
the project
9Unable to procure
video capture license
S, M42Success: We got approval from IRB for capturing data.
10Lead time of hardware
causes delays in
the schedule
S33Success: Our choice of hardware was readily
available online.
11Detection and tracking
algorithms don’t work
in different weather conditions
S, T42Success:
- We were able to process data with varying weather in
- We removed the varying weather conditions from the real
world as we were not able to capture and annotate
weather data
12Lead time of hardware
causes delays in
development/impacts the schedule
S33Success: We ordered our components well in advance