There are three key drivers of team excellence: communication, customer focus, and continuous innovation. In the first two parts of this blog series (see part 1 / part 2) on unlocking team potential, we've explored the power of feedback and how strong customer relationships can improve product quality.

In this final part, we'll look at how continuous innovation is a key component for development teams looking to gain a competitive advantage.

Embrace failure as a learning tool

The most innovative teams are those willing to try new things. Even when those ideas don't pan out, there are still valuable lessons to be learned. The best teams aren't afraid to try new things and fail because they know it allows them to learn and improve. This promotes a culture of continuous innovation that comes from seeing any misstep as an opportunity rather than a true failure.

Customer-centric innovation

The best ideas don't always come from team brainstorming sessions. No one understands the market better than your customers and they should be a resource you tap into regularly. With a deeper understanding of your customers and their needs, it can be easier to find innovative solutions with the potential to disrupt.

With a focus on continuous innovation, you can bring this customer-centric approach into your monthly and quarterly workflows. Instead of turning to the customer when it's time to do something new, speak to them at regular intervals for feedback and insight. As their needs change and the market evolves, there could be new opportunities for innovation that would otherwise be missed.

Encourage cross-functional collaboration

While communication is important, taking this beyond the core development team is the cornerstone of innovation. When you add the expertise and insights of other teams within the organization, it's possible to innovate while improving productivity. Innovation thrives when different voices come together and these fresh perspectives could be the secret to your next breakthrough.

For example, working with the marketing and UX teams early in the development process can help set good foundations for the user experience. Encouraging this collaboration from day one can also mean fewer changes in the future. Equally, working with customer service teams could yield some interesting insights into customer needs and what they might be struggling with. This allows developers to deliver projects and fixes that solve those problems—taking a lot of the guesswork out of sprint planning.

The key here is to embrace the insights from different stakeholders. Their insights from different angles of the business can lead to a breakthrough during the brainstorming process.

Experiment relentlessly

Small, rapid experiments lead to big results. The key here is to give the team space to iterate quickly to test ideas, gather feedback, and pivot if needed. Working in sprints allows developers to experiment and adapt as things change. Making small changes during each iteration helps explore new value for the customer as developers build on the MVP (minimum viable product). This allows the team to explore new ideas while making other necessary changes based on priority.

This builds flexibility into how the team works and helps drive a culture of continuous innovation. The result is a product with proven value backed up by technology.

Nurture a culture of curiosity

Experimentation comes from curiosity so it's important to weave this into your company culture. Developers who are encouraged to ask questions and try new things will be the ones most ready to innovate—and often, the most enthusiastic about their work. Encouraging curiosity and exploration creates an environment where big ideas are born.

Innovation isn't a one-time event, it's something that needs to be nurtured on an ongoing basis. Encouraging this mindset in developers and giving them the space to experiment will result in disruptive ideas as well as innovative new ways of doing things.

Remember, the most innovative teams are the ones asking 'what if?'

In the first part blog post on unlocking team potential, we looked at the power of communication. This is a vital part of team productivity but great communication extends to the customer too. Teams that continually keep the customer's priorities top of mind are those who can deliver high-quality products that exceed expectations.

In this article, we'll look at how a focus on a client's needs can guide development teams and improve customer satisfaction.

Get to the bottom of the problem

What a customer says they need and what they really need aren't always the same thing. When identifying the key pain points that will drive feature development, it's important to make sure you're getting to the root of the problem. For example, customers might ask to reduce update size to speed up over-the-air (OTA) processes. However, one of the challenges lies in the downtime, where our technology offers a significant advantage—regardless of the update size. This capability could be even more valuable to customers than the update size itself.

Asking questions and listening to the challenges the customer is facing is vital. In this example, a developer might dig deeper into why the customer wants to reduce update size because focusing on downtime may be more effective. Taking this active listening approach leads to solutions that deliver real value for the customer.

Ongoing engagement

Your customers should be involved throughout the entire development process, not just at the end. They should be considered partners throughout the entire product lifecycle. Looping in customers early on helps to prevent surprises, catch any issues (or scope creep), and strengthen the connection between everyone involved.

Feedback is key

Every interaction with the customer is an opportunity to improve. While feedback between team members is important (as I talked about in part one of this series), it's also important to give the customer plenty of chances to give feedback on your progress. While this might seem like a way to hold things up, the time needed should be built into development workflows.

Even if the customer requests a change to the work done so far, this is going to be much easier to rectify than if it'd been caught later in the process. For example, if a customer needs to change the way a feature works, this is easier to complete before that feature has already been pushed to production and becomes tangled with other functions.

Gain valuable market insight

Your customers will likely interact with the market more regularly than your team. This means they'll have a deeper understanding of the current landscape and key challenges. Through regular conversations with your customers, you may be able to identify unmet needs, find new opportunities for innovation, or get a competitive edge by spotting emerging trends.

Innovative products aren't created in a vacuum. They're created in response to customer requirements, challenges, and pain points. To uncover what the market wants and what will best serve the organizations you work with, it's important to keep communication open and listen to those evolving needs.

Read our last part: Unlocking Team Potential: Continuous Innovation

As all developers know, there's pressure to balance speed, cost, and quality of projects. For those working in a high-tech, agile team, clear communication isn't just a nice-to-have—it's a vital part of any successful team.

In the first part of our series on unlocking team potential, we dive into the things that can boost team performance to consistently deliver quality code.

Align on the 'Why'

A successful team will always understand why they are doing something. Whether it's the reasoning behind a new feature or the overall objective of the project, everyone should understand the purpose of what they are working on. This allows team leaders to get proper buy-in from developers, as well as executives.

Speak one language

Speaking one language is about finding a common vocabulary to discuss the project at all levels. And it's not just developers who need to be on the same page but all stakeholders involved in the process. This means everyone understands the objectives, progress, and expectations.

Focus on MVP

It's tempting to aim for perfection but it's far more effective to define a minimum viable product (MVP) and then work iteratively on new features and updates. Proper communication during this process is vital as everyone needs to understand what the properties and trade-offs are. This helps the team avoid getting bogged down by perfectionism, allowing them to deliver value quickly.

Document, document, document

It's important to document as if your product depends on it—because it does! Clear, up-to-date documentation is the guiding force for every decision and task. This is important during the early stages of any project but becomes even more vital when it comes to delivery updates or fixing bugs.

Team leaders should properly communicate the importance of this at all stages. Give developers the space to include documentation duties in their usual workflows during a sprint to ensure these important tasks are completed alongside other tasks.

Feedback is a two-way street

Proper communication is built on a culture where everyone feels comfortable giving honest feedback in all directions. This fuels continuous improvement but also helps avoid roadblocks in the future. Build in time for developers to share feedback but make it clear that it's also welcomed at any stage during the process.

Listen

Your team, customers, and stakeholders could hold the key to innovation. This is why it's so important to listen. Direct feedback can be useful and it's important to take this on board but listen for the meaning behind all communications. A customer talking about a challenge they're currently dealing with could be an opportunity to add something new to a contract or a chance to go above and beyond for them.

The most groundbreaking products don’t come from the loudest voices—they come from teams that value communication and collaboration. When team members listen to each other’s feedback and understand the purpose behind their work, they can achieve even more together.

Read our next part: Unlocking Team Potential: Focus on the customer

In the early 2000s, the mobile phone industry underwent a shift, spearheaded by the introduction of the iPhone. This revolution wasn't just about hardware, it was the combination of software and user experience that set Apple apart. Today, a similar revolution is happening in the automotive industry, particularly among electric vehicles (EVs). This transition, initially dominated by Tesla, is now pivoting towards a new paradigm where automakers are focusing on the digital user experience as much as the physical features of the vehicle.

The Rise of Electric Vehicles: A Hardware-Centric Approach

The beginning of the mobile phone revolution saw a focus on the hardware. With more players in the market, it was important for traditional mobile phone manufacturers to focus on features and technology to stand out. This saw the introduction of slimline models, QWERTY keyboards, and later touchscreens.

The beginning of the EV revolution was akin to the advent of the smartphone. Drivers were interested in battery life (akin to mobile phone battery capacity), charging infrastructure (similar to network coverage), and physical performance metrics. Tesla, much like Apple, was a front-runner, not only for its battery technology but also for its ability to balance this with a unique user experience.

Chinese manufacturers, paralleling companies such as Samsung and Huawei in the mobile phone arena, quickly followed suit. They emphasized not just the hardware but also affordability, rapidly expanding the EV market's scope and accessibility. Our recent Automotive Software Survey showed that 31% of people would consider an EV from a Chinese manufacturer, with 21% of those stating the reason was that the price was attractive. 

This shows that the EV market is becoming more competitive with Chinese manufacturers able to compete on price in a way that others aren’t always able to. This means legacy manufacturers are looking for ways to increase their competitive advantage.

Software: The New Player in Automotive Innovation

As EV hardware matures and becomes more standardized, the distinguishing factor shifts to the digital user experience. This is determined by the vehicle’s software and how it serves the overall experience of the vehicle — an echo of the mobile revolution where iOS and Android defined user preferences.

The software in an EV encompasses everything from the intuitiveness of the infotainment system and the sophistication of autonomous driving features to the personalization of the driving experience. A vehicle’s software functions can be the thing that separates a good vehicle from a great one and users are beginning to pick up on this as they shop for their next car.

User Experience: Driving Customer Loyalty

In the mobile industry, Apple's success was due to its product and ecosystem. The seamless integration between hardware, software, and services (like the App Store, iCloud, etc.) created a loyal customer base. A study found that Apple has the most loyal customers — 92.6% of iPhone users plan to stick with Apple for their next phone, compared to 74.6% of Samsung users.

In the automotive world, a similar trend is emerging. Manufacturers are not just selling cars; they're offering a holistic driving experience that extends beyond the vehicle. This will only increase loyalty as drivers get used to the user experience of their favored brand.

This all comes down to functionality such as over-the-air updates that refresh the vehicle's capabilities, apps that control the car’s features remotely, and even subscription-based services that unlock additional features. The focus is shifting towards creating an ecosystem where the car is an extension of the driver’s digital life.

Some automakers are moving away from siloed third-party systems such as Android Auto and Apple CarPlay and are instead focusing on creating intuitive native infotainment systems. While consumers might want easy integration with familiar services, this shouldn’t come at the expense of the in-cabin experience.

Automotive strategist and influencer James Carter recently spoke about this on LinkedIn, praising Rivian and Tesla for their infotainment systems. He said: “Both took the time to develop a ground up solution that is fully integrated with other features, such as Supercharger location details, ideal charge time and alternate route ideas. Everything you need is right there on the screen. What’s more, the maps are fast and the overall experience is seamless.”

According to our Automotive Software Survey, 40% of automotive professionals feel there’s going to be a shift in the industry to embrace Tesla-like continuous quality processes within five years. However, many manufacturers seem to be struggling to match what Tesla has been able to do. In 2022, 55% of automotive professionals thought the shift would come in five years. Now, more respondents than even think this will come within 10 or 15 years.

https://www.auroralabs.com/2023-survey-results/

Challenges and Opportunities

This shift isn't without challenges. Traditional automakers must adapt to a software-first approach, which differs from their traditional mechanical expertise. This opens up opportunities for new players, much like the mobile revolution, where many traditional phone manufacturers couldn't adapt to the smartphone era.

With software as a competitive differentiator, many automakers need to balance new hurdles with the traditional challenges of automotive manufacturing. Fisker, the US-based EV maker, filed for Chapter 11 bankruptcy at the end of June 2024. While the company intends to keep serving existing customers and building a network in Germany, there was a critical issue with its software 3.0 deployment. The update compromised several large data volumes, which frequently drained the vehicle’s small 12V battery.

For other automotive professionals, safety is, as ever, a big concern with 27% of respondents to our Automotive Software Survey stating that ensuring safety and reliability is one of the most challenging elements of automotive software development. Furthermore, data security and privacy are paramount, just as they are in the mobile industry. Consumers will demand transparency and control over their data, and regulations will likely follow.

The automotive industry is at the cusp of a revolution that will change how people buy cars. The companies that will dominate this new landscape are those that will understand the importance of software and user experience. They will be the ones to create not just vehicles, but holistic, connected, and personalized driving experiences. Just as Apple reshaped our perception and use of mobile phones, we await the visionary companies that will redefine our concept of the automobile in the era of electric vehicles.

商用車には、一般乗用車よりも求められる要件が多くあります。具体的には、車両サイズ、走行距離、物流現場における複雑な自律走行などです。これら商用車の将来性を広げるため、数多くの試みが行われています。

商用車の世界では、データ収集能力と直面する独自の課題によって驚くほどの革新が起こっています。この記事では、これらの進歩のいくつかについて詳しく取り上げ、今後の展望について考察します。

運転支援システム

一般乗用車と同様に、トラックにも先進運転支援システム (ADAS) が搭載されています。商用車向け ADAS は、一般乗用車に搭載されているものとほとんど同じです。つまり、車線維持支援や衝突軽減といった安全性を向上させるための機能を備えています。

ただし、トラックは乗用車よりも大きく重いため、長時間の停止中にドライバーの疲労を防ぐためのブレーキホールドモードや、オートホールド (Freightliner の Cascadia 専用機能) といった追加の安全機能も備えています。これらは、運転手が運転不能になった場合に、トラックが自然に停止するのを待つのではなく、自動的にブレーキをかけ車線内で安全停止させるための機能です。

将来的には、ハードウェアとソフトウェアの両方の改良により、これらの機能がさらに洗練されたものになると考えられています。つまり、より離れた距離からでも危険を識別できるようになるはずです。したがって、トラックの運転手や周囲の道路利用者の視認性と安全性が向上します。

自動運転と隊列走行

現在、業界では完全な無人運転車よりも、自律走行トラックの実現が近づいています。その理由は、商用車がほとんどの時間を走行するのは高速道路上だからです。高速道路上だと、道路標識、周囲の状況、実行する操作の種類は一般道に比べて複雑ではありません。つまり高速道路上は、他の車両、歩行者、動物がどこからともなく現れるような狭い市街地や狭い田舎道よりも予測がしやすい環境といえます。

自動運転トラックの走行テストの多くは、未だに何か問題が発生した場合に運転手が運転を引き継ぐ前提で行われていますが、今まで見たことのない形での自動運転が可能になりつつあります。LIDAR、カメラ、レーダー システムを最大限に活用する新しいハードウェア 革新と、より強力な ECU を組み合わせることで、無人運転トラックによる 24 時間走行が実現される可能性も出てきており、これは人間では不可能な運転の実現が近づいています。

まだ実用化はされていませんが、トラックの隊列走行試験からも有望な結果が得られています。隊列走行を行うと、トラックの車列を互いに近づけて走行させたり、空気抵抗を減らしたり、燃費を改善したり、道路上のスペースを今までより解放したりできます。ただし、これは完全な自律走行ではなく自動走行です。車両が車列から離れて目的地まで進む必要がある場合は、依然として運転手が必要となります。

予測メンテナンス

AIの活用により、車両管理者は商用車のメンテナンスが必要になる時期をより正確に把握できるようになります。車両内のセンサーと AI ツールを使用することで、商用トラックにメンテナンスが必要かどうかを正確に予測できます。これにより、技術者を現場に派遣する必要がなくなり、拠点に戻った後の修理で対処できるようになります。

車両のメンテナンスの必要な時期を予測できるので、ダウンタイムの最小化や、修理時における代替車両の手配がしやすくなります。くわえて、こうした効率の良いメンテナンスを実施することによる安全性の向上も期待できます。たとえば、タイヤセンサーと予知保全アルゴリズムを組み合わせることで、タイヤのパンクを防ぐことができます。

ソフトウェアの革新

ハードウェアは重要ですが、これらすべてのスマートな機能を実行するのはソフトウェアです。こうした機能を搭載するとシステムは極めて複雑になることから、確実に問題なく動作させるにはソフトウェアの革新が必要となります。 

トラックの運用期間全体にわたって確実にシステム統合の検証を行うには、ソフトウェア開発ツールが必須となります。OTA（Over-the-Air）アップデートはゼロダウンタイムで実行され、トラックの生産性を中断することなく行われるべきです。さらに、トラックが走行中であってもソフトウェアの挙動を継続的に監視する必要があります。これにより、システムの故障が車両のダウンタイムを引き起こす前に検出されます。

AI の使用が、この開発および保守プロセスにおける重要なパートとなります。ソフトウェアのコード行間、動作、車両内の関係性の変化を検出するのに役立ちます。

これにより、開発プロセスが迅速化されるだけでなく、アップデートや追加機能の市場投入時間も改善されます。Aurora LabsのVehicle Software Intelligenceは、商用車向けのソフトウェア開発の課題のいくつかを解決するのに役立ちます。詳細を知りたい場合は、こちらでデモを予約してください。

自動車ソフトウェアの急速な進化の中で、革新と業界標準のバランスを取ることは複雑な課題です。人工知能（AI）やディープテックの台頭により、この安全に関わる産業におけるコンプライアンスの必要性はさらに重要になっています。Aurora Labsは、このバランスの重要性を認識し、Automotive SPICE標準に適合するために、Kugler Maag Cie by UL Solutionsとの18ヶ月の協業を開始しました。

Aurora Labs は最先端の AI テクノロジーの開発にとどまらず、自動車ソフトウェアの品質と安全性の向上に深く取り組んでいます。同社の Vehicle Software Intelligence技術により、複雑なデータを開発者向けの強力なツールに変換できます。これにより、ソフトウェアの依存関係をより適切な検出し、テストカバレッジ範囲の拡大、ソフトリリースの高速化、ソフトウェア自体の信頼性向上を実現できます。

Kugler Maag Cie by UL Solutions との連携

Kugler Maag Cie by UL Solutionsは、イスラエルの自動車用人工知能（AI）企業であるAurora Labsと密接に協力し、自動車テクノロジーセクターで競争力のあるプレーヤーになるための指導を行い、ASPICE CL-2規格に適合できるように導きました。

同社は、まずAurora Labsの改善すべき領域を特定するために包括的なギャップ分析を行うことから始まりました。これらの調査結果に基づき、週次のワークショップを実施し、特定された課題を克服するために必要な知識、ツール、および戦略を提供しました。

それにより、以下を実現できました。

• 業界標準に沿ったプロセスの更新
• 新しい品質ツールの統合
• 新しいワークフローに関する従業員のトレーニング
• イノベーション、スピード、品質における最適なバランス

課題と解決策

主な課題の1つは、企業のマインドセットを変えることでした。当初、Aurora Labsは自社のツールの威力を証明するために迅速な開発に焦点を当てていましたが、これはスタートアップのやり方です。しかし、成長とともに、業界標準に見合った品質重視へのアプローチに移行する必要が生じました。

Aurora Labs の共同設立者兼 CEO である Zohar Fox 氏は、「Kugler Maag Cie by UL Solutions と連携することで、他の方法よりも早く、業界水準に適合した製品を開発できました。定期的なワークショップを開催しつつ、このテーマに研究開発運営費予算の 12% を投資しました。それにより、ギャップ分析で明らかになった領域の改善に重点を置けるようになり、わずか 1 年半でこれらの課題を克服できました。同社との連携により、Aurora Labs は自動車業界の最大手企業が求める品質とプロセスに応える革新的なディープテック ソフトウェア開発プロジェクトを実現し、有望な自動車技術企業としての地位を確立することができました」と述べています。

結果

Aurora Labsは、ASPICE CL-2に準拠することで、自動車メーカーにとって信頼できるパートナーとして位置づけ、ダウンタイムの短縮、空きメモリ容量の拡大、評価テストの対応スピード向上、早期バグの発見などを通じて自動車ソフトウェアの品質向上を提供しています。業界標準に準拠することで、ソフトウェアの欠陥リスクが軽減され、製品全体の品質を向上させます。

UL Solutions の Mobility and Critical Systems 部門の Director Consulting である Steffen Herrmann 氏は、「Aurora Labs チームが短期間でこのレベルのコンプライアンスを達成できて、うれしく思います。ディープテックは複雑です。そのことを考慮すると、ASPICE 標準に準拠できるということは、同社が自動車業界における革新的なプレーヤーとして位置付けられることを意味します」と述べています。

Aurora Labs は、スタートアップ企業から大手自動車技術企業へと成長しました。それを支えたのは、同社のビジョン、コミットメント、卓越性への取り組みにあります。Kugler Maag Cie by UL Solutions との連携は、この取り組みにおける重要なマイルストーンとなります。アジャイルなカルチャーを維持したまま、業界標準への適合と期待を上回る成果を両立できたためです。

ご存じの通り、自動車ソフトウェアは業界でホットな話題となっています。モダンな電気自動車会社から伝統的な自動車メーカーまで、誰もがソフトウェアの課題と機会について活発に議論しています。

フォード·モーター社の CEO である Jim Farley 氏は、Fully Charged の最近のインタビューでソフトウェアを適切に開発することの難しさについて語っています。彼は強く主張したのは、「複数のソフトウェアプロバイダーとそれらの間において統合がなされていない」という問題についてです。フォードが電気アーキテクチャの開発を内製化することになったのは、それが理由であると述べています。

Farley 氏は、「伝統的な自動車メーカーにとって、ソフトウェアを適切に開発するのは非常に難しいのです。なぜなら、150 社もの異なる企業によってソフトウェアが作られており、互いに連携していないためです。(中略) 使用するプログラミング言語、ソフトウェアの構造がそれぞれ異なり、コード行数も数百万行に上るので、すべてを把握することすらできません。フォードでは電気アーキテクチャを完全に内製化した背景には、そうした理由があります。しかしその場合、すべてのソフトウェアを自分たちで書く必要があります。ここで問題になるのは、一般的に自動車会社はソフトウェアを書いた経験が一度もない、という事実です。これまでに一度も、です。したがって、当社では自動車を操作するためのソフトウェアを、文字通り初めて書くことになったのです」と述べています。

こうした問題は、フォードだけではなく、テスラも同様に直面しています。Farley 氏は、Twitter のスペースでのインタビューで Elon Musk 氏とソフトウェアに関する対談を行っています。テスラはソフトウェア デファインド ビークル企業においてトップですが、この際、Musk 氏も「自動車のソフトウェアは非常に難しい」と認めています。

ステランティスも例外ではありません。「 は過度に複雑かつ高価になっています。(中略) 起動方法すら誰かに聞かないとわからない車もあります」と、ステランティスの CTO、Ned Curic 氏は Grenoble で開催された CEA-Leti の Innovation Day での講演 で述べています。

シトロエン、フィアット、プジョー、アルファロメオ、オペル、ダッジ、ジープ、ボクスホールのブランドで自動車を製造しているステランティスは、現代の自動車がいかに複雑になっているかをよく認識しています。同講演中、Curic 氏は次のように説明しています。「より少ないリソースでより多くの成果を上げる方法を考え出す必要があります。キャビン内の 250 の機能のうち、(中略) 150 の機能を削りました。車両には 270 個のシリコン デバイスが搭載されていますが、これを 70 個にまで減らしました」

ソフトウェアについて、ここまで深く考えているブランドは他にもあります。Reuters Events の最近のレポートでは、コネクテッド ビークルとソフトウェア デファインド ビークルに一貫して焦点を当てています。会議参加者の約 60% も、この分野に興味があると答えています。

このレポートでは、大手メーカーの多くがソフトウェア開発を社内で行っていることも強調されています。アウディの CEO、Markus Duesmann 氏は、ソフトウェアに関する外部との連携は当面予定していないと説明しています。彼は、「現時点では、開発速度の低下と複雑性の増加を招くことになります。そして当社には、十分な人員と拡張性があります」と語ります。

トヨタの新 CEO 佐藤恒治氏もソフトウェアに注力しています。4 月の発表で、「最新のハードウェアとソフトウェアをつなげることで、クルマとさまざまなソフトウェアが自由につながるようになります。(当社のソフトウェア プラットフォームである) Areneは、こうした進化を支えるプラットフォームとして重要な役割を果たすことになるでしょう。当社は Woven By Toyota とともに、2026 年に向けた次世代 BEV の開発に全力を尽くします」と語っています。

他のブランドも独自のソフトウェア プラットフォームの開発に取り組んでいます。メルセデス·ベンツは、車両の他の部分とつなげ、Google や Nvidia などのテクノロジー企業と連携して構築する新しい MB.OS インフォテインメントシステムの導入を予定しています。メルセデスの CEO、Ola Källenius 氏は、「私たちは、世界で最も魅力的な車を作ることに全力を尽くしています。当社は、自社のオペレーティング システムを自分たちで設計することにしました。チップからクラウドまで、車両のハードウェアおよびソフトウェア コンポーネントに対して完全なアクセスと活用が可能な、独自のアーキテクチャです」と語ります。

AI は実用的な洞察を生み出す

これらの業界リーダーの発言はすべて、メーカーが直面している課題に対処できる解決策が求められていることを示唆しています。AI ベースのツールは、要件からコーディング、継続的インテグレーション、継続的デプロイメント (CI/CD) に至るまで、これらの多くのハードルを克服するのに役立ちます。

自動車メーカーが開発プロセスを加速させ、ソフトアップデートをより早くリリースする方法の 1 つは、Aurora Labs が提供する AI ツールを使用することにあります。これにより、ソフトウェア開発ライフサイクルへのフォーカスとトレースが可能になり、開発からテスト、導入まですべてがスピードアップします。

これらの課題を解決するプロセスには、大きな機会が眠っています。フォード、テスラ、メルセデス·ベンツ、VW、トヨタ、ステランティスなどの業界大手はいずれもこの問題に正面から取り組んでおり、ソフトウェア開発の内製化の価値と課題を認識しています。自動車ブランドがソフトウェアを社内で開発する場合でもアウトソーシングする場合でも、成功の鍵は、ソフトウェアの開発と保守のプロセスで AI ベースのツールが果たす役割を理解することにあります。

常にイノベーションが求められている中、自動車業界のすべての企業は、将来の自動車に不可欠な要素としてソフトウェアの役割をよく考える必要があります。AI を活用して、より迅速かつ効率的な開発サイクルを実現することは、常に進化する市場で存在感と競争力を維持するための鍵となります。メーカーが直面している大きな課題を AI がどのように解決できるかについて詳しく知りたい場合、右記のホワイトペーパーをダウンロードしてください。自動車業界における 5 つのソフトウェア開発の課題と、AI がそれらにどのように対処しているのか。

As the world advances in the age of artificial intelligence – particularly generative AI – it might feel as if there are androids among us. Artificial intelligence that can generate words and pictures, understand and respond to conversations, and perform tasks is a crowning achievement. At least it feels that way now, only time will tell where things will go next.

When we were children, we were always asked: “What do you want to be when you grow up?” There was always a wide range of answers – one wanted to be a veterinarian, the other an astronaut, and so on. But if we could ask AI this question, what would it say?

Perhaps one of the most iconic AI characters is Lt. Commander Data (played by Brent Spiner), from the TV series Star Trek: The Next Generation. In it, Mr. Data helps with calculations and problem-solving, all with the speed and accuracy of an android. What often lets him down, however, is that he’s unable to understand and master human emotions. While his perception and access to information are all huge strengths, Data wants something incredibly human.

AI in Automotive Today

Today, artificial intelligence is one of the hottest topics in the world and it’s giving us an idea of what AI might want to be when it grows us. The automotive industry is embracing this technology in everything from its production line to vehicle software. It starts with the adaptation of in-car systems, such as ChatGPT by VW, Tesla’s AI-powered Autopilot, or the MBUX infotainment by Mercedes. There’s no doubt that, in time, it will advance further into our vehicles and to more safety-critical elements, as well as into human-car interaction, route-planning, and more that, right now, we can only dream of.

Here are some of the areas AI is already being used in the industry:

1. Production line:

   1. Robots that build vehicles and are able to detect defective materials
   2. Parts warehouse management robots that AI in use sensing and routing

2. Accident prevention:

   1. In-cabin driver awareness - falling asleep, DUI, distractions off-road 
   2. Pattern learning of dangerous driver behavior
   3. Obstacle identification
   4. Adaptation of car limitations according to weather conditions

3. Maintenance: 

   1. Preventing car breakdown on-road by learning symptoms in advance.
   2. Car smart usage - reduce wear and tear.

4. Fleets:

   1. Learning and plotting optimized distribution routes
   2. Detecting and identifying defects for car rental companies

In addition, here are three AI-driven areas of specific interest to me:

Autonomous Vehicles

You don’t get very far in a conversation about AI in automotive without touching on autonomous vehicles. 

There are five levels of automation with level one vehicles being able to handle single tasks such as automatic braking while level five is fully autonomous capabilities without the need for driver presence. 

Today we are at level 2 with advanced driver assist systems (ADAS) providing accident prevention capabilities such as forward collision warning (FCW), lane-keep assist, adaptive cruise control, and more. This enables independence but still needs to be monitored by the driver.

To achieve this, the vehicle needs to “see” the world outside and understand it. It does this through cameras, LiDar, radar, IR sensors, and more. With all this information, the vehicle can make small decisions such as keeping the car in the lane if it drifts out of the white lines.

As well as good hardware, this is only possible with the right software to accompany it, otherwise the vehicle won’t know what to do with the information the sensors and cameras are feeding it. 

The world is aspiring to get to a point where all vehicles are fully autonomous (level five). This would mean all cars talk to one another through Vehicle-to-Vehicle communication (V2V) and the infrastructure around them through Vehicle to Everything (V2X). Once we get to this point, all you’ll need to do is get in a car, tell it where you want to go, and let the AI do the rest.

Vehicle Insurance 

What if an insurance company could adjust insurance fees according to the behavior of the driver?

Usage-based insurance looks at your behavior as a driver and adjusts the price accordingly. This means safer drivers will have lower premiums than those considered more at risk. Previously things like black-box insurance have made this possible, but now insurers are exploring AI to facilitate this. 

According to McKinsey, 10% to 55% of roles within insurance could be replaced by AI in the next 10 years – particularly underwriting, claims, and finance. In the future, almost all claim and fix processes will be managed by AI, reducing human involvement to the minimum, and maybe even reducing the costs for us consumers.

Just like autonomous vehicles, this requires sophisticated software to be successful. However, as insurance companies are dealing with sensitive data, security is paramount. All the details of a driver need to be aggregated to a server to help teach the AI and inform its outcomes. Disregarding errors and security risks in the software could lead to noncompliance, legal issues, lost revenue, and poor brand reputation.

Predictive Vehicle Maintenance

When a car breaks down, there's nothing to do but fix it – sometimes making the car unusable and maybe even stuck somewhere, something that is extremely expensive for commercial fleet companies. But what if we could know what’s going to become an issue before it breaks? Proper maintenance of a vehicle will always help to keep breakdowns to a minimum but AI can take this to the next level with Predictive Maintenance.

This is especially useful across fleets where keeping track of each individual vehicle can be challenging. AI can study how each vehicle is being used, monitor driver behavior, and begin to learn trends that could contribute to breakdowns. This will ensure fleet managers can minimize downtime while keeping on top of vehicle maintenance.

This technology can also take some of the unknown out of purchasing a second-hand car. With AI, the buyer could validate the health of the vehicle and see if any major breakdowns are around the corner. This can help them make a buying decision and potentially save a huge amount of money when looking for an affordable used vehicle.

In the future, we will see completely automated maintenance where the car will not need to get to the garage at specific times in the life of the vehicle, and the entire BOM (Bill of Material) for the car’s maintenance will be known ahead of time, lowering storage needs and enabling more efficient garage working hours.

Throughout the years, Mr. Data may not have been able to master human emotion but came close to learning to mimic this ability in his own way – or, of course, use a very buggy emotion chip. AI will probably be the same. It might be a good replacement for a lot of human tasks and maybe even perform better in some cases, but there will always be a limit.

During Star Trek: TNG, even with Mr. Data’s great programming, he still was prone to bugs and misuse of his abilities. According to Star Trek, computer bugs and cyber threats are still a real problem in the 24th century, and we have no reason to doubt the logic of the show’s writers. AI has real potential but it has to be used in a way that plays to its strengths.

Aurora Labs has developed an LCLM (Large Code Language Model)  that works at the line-of-code (LOC) level at runtime, this enables us to identify deviations and anomalies at a very basic level that can discover not only coding bugs but software functionality misbehavior as well. It can monitor the software in real-time to detect changes in the software’s behavior before these escalate to become critical system errors. Raising a flag before a system fails will not only ensure that the devices continuously learn and improve but could also save lives in devices such as cars or trucks. 

While emotions may be a step too far for AI-based devices, self-healing is one form of human nature that I truly believe can be achieved.Find out more about Aurors Labs’ technology here: https://www.auroralabs.com/product-overview/

The automotive industry has traditionally been a bastion of structured development. Yet, as the digital age advances, there’s a growing need for more flexible practices. Unlike more traditional development processes, the agile methodology is non-linear and allows for increased adaptability — especially when making last-minute changes.

This is the final article in our three-part series (part 1| part 2) where we explore how AI impacts the automotive industry. In the first, we dove into the need for new tools in the software development process while the second looked at the need for innovation in process as well as technology.

Embracing controlled agility 

Agile development in automotive isn't about recklessly pushing new versions — though it might work that way in other industries. Cars are safety-critical machines, and there's no room for error. But this doesn't mean the industry can't benefit from an agile approach. 

By implementing controlled agile processes, developers can ensure small updates don't adversely impact the entire vehicle. This approach allows developers to make small, iterative changes while still meeting automotive industry regulations.

Benefits of iterative development 

From a developer's standpoint, the agile approach offers many advantages. Humans find it challenging to tackle large tasks head-on so it’s practical to break things down into more manageable pieces. When presented with a large task, like developing a brand new function, it can feel overwhelming. Breaking this down into smaller, more manageable chunks better fits natural human behavior, allowing for more productivity and flexibility. 

Developers can be more efficient by focusing on specific functions in phases and releasing them in small steps. This iterative process not only increases productivity but also ensures that each function is thoroughly tested before proceeding. This is vital when it comes to vehicle safety.

How AI tools can support the agile development process

Artificial intelligence tools have the potential to streamline the development process. For instance, consider the task of tracking bugs in a system. Traditional systems often require manual searches, leaving developers feeling like they’re looking for a needle in a haystack. 

AI can assist in understanding customer needs, generating tests, and ensuring that these tests align with requirements. This technology can help developers resolve errors more quickly by using AI to map the entire software system and give insights into exactly which lines of code have changed, which need testing, and which are causing issues. This helps to track down bugs, including those from unpredicted scenarios and edge cases that might otherwise be difficult to find.

Automating these aspects allows developers to be more agile in their software development and testing by getting faster quality feedback and enabling them to focus on what they do best: developing innovative solutions for the automotive industry.

The automotive industry is on the cusp of a significant transformation. As software-defined vehicles become the norm, the need for agile development becomes more important than ever. By integrating these methods and leveraging AI tools, developers can better innovate while improving efficiency.

Click here for more insights into the future of automotive software development.

Part 1| Part 2

As the automotive industry grapples with the challenges of modernization, it's essential to understand that innovation in automotive software isn't just about the development of new features. It's equally about refining and redefining the processes that support it.

This is the second article in our three-part series where we explore how AI impacts the automotive industry. In the first, we dove into the need for new tools in the software development process.

The need for process innovation

Software innovation is undeniably crucial, especially as consumers begin to demand more high-tech features such as autonomous driving capabilities. However, the real challenge lies in ensuring that developers are also innovating the processes used to build automotive software. Changing human behavior, especially in a legacy industry such as this, is no small feat. The serial production of software — which is akin to a manufacturing production line — may offer control but is no longer the most efficient or effective way to approach software development.

What’s needed is a more agile approach (more on this in the third part of this series), one that involves smaller steps and innovative testing methods, such as virtual environments. Complex software demands process innovation, and the industry must rise to meet this challenge.

As we mentioned in our previous article in the series, the traditional way of matching customer requirements with the finished product was through the V-shape model. This is the way things have been done for a long time but it’s time-consuming and, often, inaccurate. Innovation isn’t just about adopting new technologies, it’s about thinking outside of tradition and considering what new processes might be possible with advanced tools such as those using AI.

The cultural shift

The journey to process innovation is as much about culture as methodology. Developers, who are often bogged down by the daily grind of fixing bugs and releasing software, may overlook the need to reevaluate their processes. As more tech-forward companies bring agility and innovation to the table, however, larger OEMs are beginning to take notice. These industry giants are now seeking insights from agile startups, indicating a promising shift toward a more collaborative and innovative future.

For developers who are already thinking in this way, it’s a case of looking at requirements, development, and testing, then considering how those processes could be improved using technology or new ways of working. 

A good way to start thinking about this is in terms of the challenges. Within the traditional methods of development, what isn’t working? Perhaps the testing process takes too long or it’s difficult to get updates out on time. Maybe it’s external problems such as supplier deliveries that are causing issues. Whatever it might be, an innovative approach to the process could be the answer, especially when backed up by technology.

For example, if the testing process feels cumbersome, the question should be asked, is running all tests, no matter the software changes, the best way of achieving software quality? Switching to a process that incorporates AI to detect the changes in the software and analyze the potential quality risks could be the solution. For example, Auto Detect from Aurora Labs can efficiently select which tests have the highest probability of failure due to the changes made in the software in the specific build. Rather than running all tests available, this means only the necessary ones will run, significantly reducing time while still ensuring test effectiveness.

Shifting left

The cost, both in terms of money and resources, of detecting and fixing software problems increases as the software progresses through the software lifecycle. For example,  traditional methods of software updates come with certain limitations, especially when it comes to the speed at which manufacturers can release updates. For instance, updating car software traditionally involves inefficient processes that are integrated and implemented after the software has been developed and installed in the vehicle ECU – a cumbersome and data-intensive process that’s far from cost-effective. However, AI technology, such as Aurora Labs’ Auto Update, now allows for software updates to be integrated as an integral part of the software development process and not as an afterthought. 

To leverage the power of this technology, it’s important to build the tools into the Continuous Integration and Continuous Deployment (CI/CD) process. This not only makes the process more efficient but also paves the way for faster and more agile software development.

As the automotive industry continues its journey into the digital age, the balance between innovation and process will remain at its heart. By embracing agile methodologies, creating a culture of continuous improvement, shifting left with new technologies, and integrating AI tools, legacy automakers can ensure they keep ahead of the curve in a rapidly evolving industry. 

If you’d like to learn how artificial intelligence could bring innovation to your software development processes, get in touch here.

Part 1| Part 3