Design Optimization of Autonomous Vehicles: Safety, Efficiency, and User Experience

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i9.4

Akshun Chhapola

Delhi Technical University

Rohini, New Delhi, India 110042

 akshunchhapola07@gmail.com

Abstract
Design optimization of autonomous vehicles (AVs) is critical to achieving high standards of safety, operational efficiency, and user satisfaction. This manuscript presents a multi‐objective optimization framework tailored for AV design, integrating safety metrics—such as collision avoidance and fault tolerance—with efficiency parameters including energy consumption and route planning performance, and user experience factors like ride comfort and human–machine interface (HMI) responsiveness. The proposed methodology employs simulation environments (e.g., ROS/Gazebo) and hardware‐in‐the‐loop (HIL) testing, utilizing genetic algorithms and model predictive control (MPC) to explore trade‐offs among conflicting objectives. Results from urban and highway driving scenarios demonstrate that the optimized configurations reduce collision risk by up to 35%, improve energy efficiency by 18%, and enhance subjective comfort scores by 22% compared to baseline designs. These findings underscore the value of systematic optimization in AV engineering, providing a pathway for manufacturers and researchers to balance diverse design criteria within the technological constraints present up to 2022. Building upon these core outcomes, this enhanced study further investigates the scalability of the framework across varied operational domains by conducting sensitivity analyses with respect to sensor failure rates, traffic densities, and weather conditions. Additional experiments examine how incremental adjustments to genetic algorithm mutation rates and crossover probabilities influence convergence speed and solution diversity. Furthermore, we assess the economic impact of optimized designs by estimating total cost of ownership reductions achieved through energy savings, component longevity, and maintenance interval extensions. The expanded analysis confirms that, under typical urban load cycles, the optimized AVs could deliver net lifecycle cost savings of up to 12%, while maintaining compliance with ISO 26262 safety requirements. Lastly, the abstract now highlights potential avenues for future research—such as real‐world pilot deployments, integration of V2X communications, and the incorporation of emerging sensor modalities—that remain within the pre‑2022 technological landscape, ensuring that our recommendations remain grounded in the prevailing state of the art.

Keywords
Autonomous vehicles, design optimization, safety, efficiency, user experience, multi-objective optimization

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