  The Logical Execution Time (LET) model has been gaining industrial attention
because of its timing and data-flow deterministic characteristics, which
simplify the computation of end-to-end latencies of multi-rate cause-effect
chains at the cost of pessimistic latencies. In this paper, we propose a novel
method to reduce the pessimism in the latencies introduced by LET, while
maintaining its determinism. We propose a schedule-aware LET model that
shortens the lengths and repositions LET's communication intervals resulting in
less pessimistic end-to-end latencies. By adding dependencies between specific
task instances, the method can further reduce the pessimism in the latency
calculations of the LET model. If needed, e.g., for legacy reasons, our method
can be applied to a subset of tasks only. We evaluate our work based on real
world automotive benchmarks and randomly generated synthetic task sets. We
compare our results with previous work and the LET model. The experiments show
significant reductions of worst-case data age and worst-case reaction latency
values.
