How can problem solving skills improve autonomous vehicle reliability?

Autonomous vehicles (AVs) are expected to revolutionize the transportation industry, but they also pose significant challenges for reliability and safety. AVs rely on complex software, sensors, and communication systems to navigate the dynamic and unpredictable road environment. Any malfunction, error, or failure in these components can have serious consequences for the passengers, other road users, and the reputation of the manufacturers. Therefore, it is essential for automotive engineers and technicians to develop and apply problem solving skills to improve the reliability of AVs. In this article, we will discuss how problem solving skills can help identify, analyze, and resolve the common issues that affect AVs.

1 Problem definition

The first step in problem solving is to define the problem clearly and accurately. This involves gathering relevant information, understanding the requirements and constraints, and framing the problem in a specific and measurable way. For example, if an AV experiences a sensor failure, the problem definition should include the type, location, and frequency of the failure, as well as the impact on the vehicle performance and safety. A clear problem definition helps narrow down the scope of the investigation and avoid unnecessary or irrelevant actions.

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Shivesh K.

Hero MotoCorp Limited | ISB Co'23 (Brand Ambassador) | INSEAD | Product Management | Marketing Strategy | Business Development | Project Management | Innovation | Automotive R&D
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Problem-solving excellence in autonomous vehicles starts with a clear definition. Gathering pertinent data and understanding constraints are vital in framing specific, measurable issues. In the case of an AV sensor failure, defining the problem includes specifying type, location, frequency, and impact on performance. A precise problem statement is key, aiding focused investigations and eliminating unnecessary actions. Let's leverage robust problem-solving skills and drive innovation on the road!

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2 Root cause analysis

The next step in problem solving is to find out the root cause of the problem. This involves using various tools and techniques, such as data analysis, testing, simulation, brainstorming, and fishbone diagrams, to identify the factors that contribute to the problem and how they interact. For example, if an AV has a communication problem, the root cause analysis should examine the hardware, software, network, and environmental factors that affect the communication quality and reliability. A thorough root cause analysis helps pinpoint the source of the problem and eliminate the symptoms.

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Shivesh K.

Hero MotoCorp Limited | ISB Co'23 (Brand Ambassador) | INSEAD | Product Management | Marketing Strategy | Business Development | Project Management | Innovation | Automotive R&D
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In the dynamic world of autonomous vehicles (AVs), problem-solving skills act as the secret sauce for unrivalled reliability. Picture AV glitches as unsolved mysteries; adept problem solvers go beyond superficial fixes. They conduct a thorough root cause analysis, utilizing data analysis and simulations to pinpoint issues like buggy sensors or software glitches, enabling permanent solutions. These skills foster creativity, empowering engineers to explore diverse solutions and adapt AVs for unexpected scenarios. With constant evolution through learning from every challenge, AVs become adept navigators of real-world complexities, addressing glitches one step at a time. 🌐🚀 #AutonomousVehicles #ProblemSolving #Innovation #TechExcellence

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3 Solution generation

The third step in problem solving is to generate possible solutions to the problem. This involves using creativity, logic, and knowledge to come up with different options that can address the root cause and meet the requirements and constraints. For example, if an AV has a software bug, the solution generation should consider various ways to fix, update, or replace the software, as well as the feasibility, cost, and risk of each option. A diverse and comprehensive solution generation helps explore the potential outcomes and trade-offs of each solution.

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4 Solution evaluation

The fourth step in problem solving is to evaluate the solutions and select the best one. This involves using criteria, such as effectiveness, efficiency, reliability, safety, and customer satisfaction, to compare and rank the solutions. For example, if an AV has a sensor calibration problem, the solution evaluation should assess how well each solution can improve the sensor accuracy and performance, as well as the time, resources, and expertise needed to implement each solution. A systematic and objective solution evaluation helps choose the optimal solution that meets the needs and expectations of the stakeholders.

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5 Solution implementation

The fifth step in problem solving is to implement the solution and monitor the results. This involves planning, executing, and documenting the actions needed to solve the problem, as well as verifying, validating, and testing the solution. For example, if an AV has a battery problem, the solution implementation should follow the steps to replace or recharge the battery, as well as check the battery status and performance. A careful and detailed solution implementation helps ensure the quality and consistency of the solution and avoid errors or defects.

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6 Solution improvement

The final step in problem solving is to improve the solution and prevent the problem from recurring. This involves reviewing, analyzing, and learning from the problem solving process, as well as identifying and implementing improvements or preventive measures. For example, if an AV has a navigation problem, the solution improvement should examine the feedback, data, and lessons learned from the problem solving process, as well as propose and apply changes or enhancements to the navigation system. A continuous and proactive solution improvement helps increase the reliability and efficiency of the solution and reduce the likelihood and severity of future problems.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Danijel Andrijic

CIO / Technology Strategist / Speaker
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Imagine a world where traditional engineering is merely a co-pilot, and AI is steering the innovation of autonomous vehicles. Yes classical engineering is the bedrock of safety and reliability. Yet, here's the provocative truth: without embracing the raw power of deep learning, we might just be idling in the slow lane. Deep learning is not the future; it's the present, accelerating us towards unimaginable horizons. Are we bold enough to embrace this shift or will we cling to the past, watching the future pass us by?

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How can problem solving skills improve autonomous vehicle reliability?

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1 Problem definition

2 Root cause analysis

3 Solution generation

4 Solution evaluation

5 Solution implementation

6 Solution improvement

7 Here’s what else to consider

Automotive Manufacturing

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