Real-time safety assessment of trajectories for autonomous driving
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Autonomous vehicles (AVs) must always have a safe motion to guarantee that they are not causing any accidents. In an AV system, the motion of the vehicle is represented as a trajectory. A trajectory planning component is responsible to compute such a trajectory at run-time, taking into account the perception information about the environment, the dynamics of the vehicles, the predicted future states of other road users and a number of safety aspects. Due to the enormous amount of information to be considered, trajectory planning algorithms are complex, which makes it non-trivial to guarantee the safety of all planned trajectories. In this way, it is necessary to have an extra component to assess the safety of the planned trajectories at run-time. Such trajectory safety assessment component gives a diverse observation on the safety of AV trajectories and ensures that the AV only follows safe trajectories. We use the term trajectory checker to refer to the trajectory safety assessment component. The trajectory checker must evaluate planned trajectories against various safety rules, taking into account a large number of possibilities, including the worst-case behavior of other traffic participants. This must be done while guaranteeing hard real-time performance since the safety assessment is carried out while the vehicle is moving and in constant interaction with the environment. In this paper, we present a prototype of the trajectory checker we have developed at IVEX. We show how our approach works smoothly and accomplishes real-time constraints embedded in an Infineon Aurix TC397B automotive platform. Finally, we measure the performance of our trajectory checker prototype against a set of NCAPS-inspired scenarios.
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