Recent research on braking control strategies for battery electric vehicles (BEVs) commonly limits the front-to-rear braking force distribution. However, no public evidence shows that production vehicles observe these limits. The present study evaluates this assumption through road tests and high-fidelity simulations for dual- and single-motor powertrains. On-road measurements acquired from a mass-produced BEV indicate that, under typical driving conditions, the force split can diverge safely from conventional design curves—including the ECE and the ideal curves. Building on these data, a comparative analysis quantifies the efficiency penalties associated with constrained braking (fixed ratios or ideal curve tracking) relative to an unconstrained, efficiency-oriented approach. In dual-motor configurations, removing the constraint recovers up to 6.8% more kinetic energy over a real-world cycle. A power loss-based method is proposed to establish a torque curve limiting regenerative braking at low-speed operation. Applied to a single-motor delivery-van model, the regenerative torque limitation increases energy recuperation by as much as 4.8% during moderate to intense decelerations. These findings provide guidance for future brake control designs, demonstrating that efficiency-focused regeneration can coexist with anti-lock braking systems to extend BEVs’ driving range without compromising vehicle stability.

Driving simulators have become essential tools for accelerating the development of advanced driver assistance systems (ADASs) and autonomous vehicles (AVs) by enabling safe, repeatable, flexible, and cost-effective experimentation across increasing levels of vehicle automation. Despite their growing adoption in both academia and industry, the recent literature lacks a comprehensive review that captures recent advancements and the expanding role of simulators in both feature-level ADAS development and fully autonomous driving research. This paper addresses this gap by presenting a systematic review of the evolution of driving simulators and their critical contributions to automotive research, testing, and validation. A structured taxonomy of contemporary simulators is introduced, encompassing fidelity, physical configuration, scale, licensing, and system integration strategies. Key application domains are examined, including driver-centred behaviour and human–machine interaction studies, traffic modelling and control, vehicle dynamics and powertrain development, and the testing of ADAS and autonomous driving subsystems across perception, planning, control, and vehicle-to-everything (V2X) communication. This review highlights driving simulators as foundational enablers for the safe, efficient, and scalable deployment of increasingly automated vehicle technologies.