LADRI: LeArning-based Dynamic Risk Indicator in Automated Driving System
Anil Ranjitbhai Patel Peter Liggesmeyer
As the horizon of intelligent transportation expands with the evolution of
Automated Driving Systems (ADS), ensuring paramount safety becomes more
imperative than ever. Traditional risk assessment methodologies, primarily
crafted for human-driven vehicles, grapple to adequately adapt to the
multifaceted, evolving environments of ADS. This paper introduces a framework
for real-time Dynamic Risk Assessment (DRA) in ADS, harnessing the potency of
Artificial Neural Networks (ANNs).
  Our proposed solution transcends these limitations, drawing upon ANNs, a
cornerstone of deep learning, to meticulously analyze and categorize risk
dimensions using real-time On-board Sensor (OBS) data. This learning-centric
approach not only elevates the ADS's situational awareness but also enriches
its understanding of immediate operational contexts. By dissecting OBS data,
the system is empowered to pinpoint its current risk profile, thereby enhancing
safety prospects for onboard passengers and the broader traffic ecosystem.
  Through this framework, we chart a direction in risk assessment, bridging the
conventional voids and enhancing the proficiency of ADS. By utilizing ANNs, our
methodology offers a perspective, allowing ADS to adeptly navigate and react to
potential risk factors, ensuring safer and more informed autonomous journeys.