A rigorous and comprehensive testing plays a key role in training self-driving cars to handle variety of situations that they are expected to see on public roads.The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap, however, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-theart simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we also present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.
Summary
As the adaptive cruise control system (ACCS) on vehicles is well developed today, vehicle manufacturers have increasingly employed this technology in new‐generation intelligent vehicles. Pulse‐and‐glide (PnG) strategy is an efficacious driving strategy to diminish fuel consumption in traditional oil‐fueled vehicles. However, current studies rarely focus on the verification of the energy‐saving effect of PnG on an electric vehicle (EV) and embedding PnG in ACCS. This study proposes a pulse‐and‐glide‐driven adaptive cruise control system (PGACCS) model, which leverages the PnG strategy as a parallel function with cruise control (CC) and verifies that PnG is an efficacious energy‐saving strategy on EVs by optimizing the energy cost of the PnG operation using Intelligent Genetic Algorithm and Particle Swarm Optimization (IGPSO). This article builds up a simulation model of an EV with regenerative braking and ACCS based on which the performance of the PGACCS and regenerative braking is evaluated; the PnG energy performance is optimized, and the effect of regenerative braking on PnG energy performance is evaluated. As a result of PnG optimization, the PnG operation in the PGACCS could cut down 28.3% energy cost of the EV compared with the CC operation in the traditional ACCS, which verifies that PnG is an effective energy‐saving strategy for EVs and the PGACCS is a promising option for EV.
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