Employing Severity of Injury to Contextualize Complex Risk Mitigation Scenarios

Abstract

Risk mitigation is an important element to consider in risk evaluation. Safety features have helped to decrease the death ratio over the years. However, to date, each driver assistance system works on a single domain of operation. The problem remains in how to use perception to contextualize the scene to fully minimize the collision severity in a complex emergency scenario. Up to now, works on cost maps have consider simple contextualized object in mitigation scenarios. For instance, the use of binary allowed/forbidden zones or, a fixed weight to each type of object in the scene. Our work employs the risk of injury issued by accidentology to each class of object present in the scene. Each class of object presents an injury probability with respect to the impact speed and ethical/economical/political factors. The method generates a cost map containing a collision probability along with to the risk of injury. It dynamically contextualizes the objects, since the risk of injury depends on the characteristics of the scene. Simulation and dataset results validate that changing the referred parameters alters the context and evaluation of the scene. Then, the proposed method allows a better assessment of the surroundings by creating a dynamic navigation cost map for complex scenarios.

Collision Mitigation in the Presence of Uncertainty

“…B, and only examples of some of these trajectories (1, 3, 4, 5, 7, 10) are used and presented here. As mentioned in Section IV the collision probability threshold and the number of required ego vehicle trajectories (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) to reach a threshold are varied. Therefore, the earliest activation of the CMS is when only one trajectory of the ego vehicle has reached 10% collision probability, and the latest is when all trajectories of the ego vehicle have reached 98% collision probability.…”

“…If an unavoidable frontal collision is determined an aggressive emergency braking command is applied. The authors of [11] proposed a probabilistic cost map that also considers injury severity. An object in the environment is assigned an injury probability according to impact speed and ethical/economical/political factors.…”

Augmented Reality on LiDAR data: Going beyond Vehicle-in-the-Loop for Automotive Software Validation

“…Based on these results, we now use this AR implementation to challenge various navigation software. A collision mitigation driver assistant based on the cost map of [18] is being developed and tested in AR on our prototype. Future developments include the ability to augment other sensors, the design of test actors reacting to the vehicle and the contextual automated generation of critical test scenarios [19].…”