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Why Time Tracking is not the Best Solution to Catch Speedsters

Malaysian authorities will begin testing a system that records the travel duration of a vehicle from one location to another using AWAS (Automated Awareness Safety System) cameras.

This means the system will capture an image of the vehicle’s license plate via the first AWAS camera and record the time. Then, this image will be compared with the image taken by the next AWAS camera, with its time also recorded and compared. If the vehicle passes the subsequent AWAS camera too early based on the calculated duration, it is deemed that the vehicle was driven too fast.

How is a driver determined to have exceeded the speed limit?

The calculation uses the formula:

Distance (km) = Time [(minutes / 60) hours] × Average Speed (km/h)

For example, let’s set the distance as 100 km and the time taken by the vehicle to travel from Toll A to Toll B. We use time because the AWAS system considers it to determine the vehicle’s average speed. Do note that time is converted from minutes to hours by dividing it by 60 (since there are 60 minutes in an hour) to express speed in km/h.

Example A:

Suppose a vehicle covers a 100 km journey in 30 minutes. The speed limit for this route is 110 km/h.

Average speed = 100 km / (30 minutes / 60) hour,

Average speed = 100 km / 0.5 hour = 200 km/h

Result: Fined! (Exceeds the speed limit)

Example B:

The vehicle covered the 100 km distance in 60 minutes.

Average speed = 100 km / (60 minutes / 60) hours,

Average speed = 100 km / 1 hour = 100 km/h

Result: No fine (Average speed did not exceed the limit).

Calculating the “Safe” Travel Duration to Comply with the Speed Limit

Since the AWAS system tracks the duration taken for a vehicle to move between two points, what is the calculated “safe” duration?

100 km = (X / 60) hours × 110 km/h

(X / 60) = 100 km / 110 km/h = 0.909 hours

Thus, X (average time) = 0.909 hours × 60 minutes = 54.54 minutes

To verify if this calculation is correct, check whether 54.54 minutes multiplied by 110 km/h equals 100 km:

Distance = 0.909 hours × 110 km/h = 100 km

TA-DA!

This means you should pass the second AWAS camera in at least 54.54 minutes after passing the first one. With this formula, you can plan your trip by calculating the required travel time and adjusting your speed accordingly.

However…

This system is not a foolproof solution, especially for those proficient in math who know how to circumvent the formulated time by speeding excessively at certain stretches and then slowing down to match the calculated time, making it appear as if they complied with the speed limit. In fact, this is a technique used by rally drivers when transitioning between Special Stages (SS) on public roads.

Or, of more concern is that many Malaysians are not math proficient and/or too lazy to calculate anything, thus not knowing the required travel duration and consequently driving either too fast or too slow. So, back to square one.

Additionally, you can no longer arrive earlier than the calculated time even if the road is completely empty, which increases travel duration, and may even contribute to traffic build up and congestion.

So, what’s your opinion?

Is the current system better, or is this new system more effective?

Use of AI to Catch Speeding Drivers

The Awareness Automatic System (AWAS) will introduce an upgraded AI-powered ‘point-to-point’ (P2P) method to measure vehicle travel duration and distance, enabling the detection of speeding violations. A trial run is expected to begin this June.

This pilot project, designed to replace the current static speed enforcement method, will monitor two highway routes:

Genting Sempah Tunnel to Gombak Rest Stop;
Senawang Toll Plaza to Simpang Ampat (Alor Gajah) Toll Plaza.

Additionally, the system will be installed at high-risk accident zones along longer stretches, such as the Menora Tunnel to Sungai Perak Rest Stop route.

Datuk Jana Santhiran Muniayan, Secretary-General of the Ministry of Transport (MOT), explained in an interview with Berita Harian that speeding drivers can be detected by toll plaza sensors if they arrive at their destination earlier than the estimated travel time.

He mentioned that a company has already been appointed to handle the proof of concept (POC) for this system without any financial burden on the government.

“The pilot will be conducted on two selected highway routes. If proven effective, we are ready to implement it fully. The system, installed at toll plazas, will record travel time based on distance coverage.”

“For example, if a driver travels from Jalan Duta (Kuala Lumpur) to Tanjung Malim (Perak) faster than the allowed time, their vehicle’s license plate will be automatically detected, and a summons will be issued,” he said.

Jana Santhiran added that the AI-powered system can also detect if a driver stops at any location to artificially extend their travel time and evade detection.

“This means drivers cannot escape being caught for speeding, as the system calculates violations based on distance and time,” he emphasized.

Earlier in January, Transport Minister Anthony Loke announced that the AWAS cameras would be upgraded with the point-to-point method, replacing the previous static enforcement approach.

Guna AI untuk Kesan Pemandu Lampaui Had Laju

Kaedah ‘point-to-point‘ (satu tempat ke satu tempat) yang dilengkapkan dalam Sistem Keselamatan Kesedaran Automatik (AWAS) dengan teknologi AI (‘artificial intelligence‘/kecerdasan buatan) bagi mengukur tempoh dan jarak perjalanan kenderaan untuk mengesan pemandu melanggar had laju, dijangka dimulakan secara percubaan pada Jun ini.

Projek rintis kaedah itu yang dirancang menggantikan kaedah statik diguna pakai selama ini, akan memantau dua laluan lebuh raya, masing-masing antara laluan terowong Genting Sempah ke hentian Rehat Gombak dan arah Plaza Tol Senawang ke Plaza Tol Simpang Ampat (Alor Gajah).

Selain itu, sistem ini akan dipasang di lokasi yang sering berlaku kemalangan di jarak laluan yang lebih panjang contohnya di lokasi Terowong Menora ke Hentian Rehat Sungai Perak.

Ketua Setiausaha Kementerian Pengangkutan (MOT), Datuk Jana Santhiran Muniayan ketika ditemui BH, berkata pemandu melanggar had laju boleh dikesan alat dipasang di plaza tol jika tiba di destinasi lebih cepat daripada anggaran tempoh sepatutnya.

Katanya, sebuah syarikat sudah dilantik mengendalikan proses bukti konsep atau proof of concept (POC) kaedah berkenaan tanpa sebarang impak kewangan kepada kerajaan.

“Projek akan dilaksana secara rintis di dua lokasi lebuh raya terbabit. Jika benar-benar efisien, kita sedia serahkannya untuk dilaksanakan. Sistem ini dipasang di plaza tol, akan ambil bacaan masa perjalanan berdasarkan jarak ke jarak.

“Contohnya pemandu dari Jalan Duta, Kuala Lumpur ke Tanjung Malim, Perak, tiba lebih awal dari masa sepatutnya, jadi nombor plat kenderaan akan dikesan dan saman dikeluarkan automatik,” katanya.

Jana Santhiran menjelaskan, sistem dengan teknologi AI itu juga boleh mengesan tempoh seorang pemandu berhenti di mana-mana lokasi jika mereka berbuat demikian untuk memanjangkan tempoh perjalanan bagi mengelak dikesan sistem itu.

“Maksudnya pemandu tidak boleh terlepas daripada dikesan jika memandu melebihi had laju, berdasarkan jarak dan tempoh masa yang diukur menerusi sistem baharu ini,” katanya.

Awal Januari lalu, Menteri Pengangkutan, Anthony Loke dilapor berkata, pelaksanaan kamera AWAS akan ditambah baik dengan kaedah point-to-point berbanding kaedah statik dilaksanakan sebelum ini.

Tempoh Pergerakan AWAS Bukan Solusi Terbaik untuk Menangkap Kesalahan Lebih Had Laju

Pihak berkuasa Malaysia akan memulakan percubaan sistem yang merakam tempoh pergerakan sesuatu kenderaan dari satu tempat ke satu tempat, dengan menggunakan kamera AWAS (Automated Awareness Safety System).

Ia bermaksud sistem tersebut akan mengambil imej nombor kenderaan itu melalui kamera AWAS yang pertama dan waktu imej itu direkod. Kemudian, imej tersebut akan dibanding dengan imej yang diambil oleh kamera AWAS yang seterusnya dan waktunya turut direkod dan dibandingkan. Jika kenderaan itu didapati melepasi kamera AWAS yang kemudiannya terlalu awal daripada pengiraan, ia bermaksud kenderaan itu telah dipandu dengan terlalu laju.

Bagaimanakah seseorang pemandu didapati telah memandu lebih daripada had laju yang ditetapkan? Ia dikira menggunakan formula:

Jarak (km) = Masa [(minit / 60) jam] x Kelajuan purata (km/j)

Sebagai contoh, kita tetapkan jarak sebagai 100 km dan kita ubahkan masa yang diambil oleh kenderaan tersebut begerak dari Tol A ke Tol B. Kita gunakan masa sebab sistem AWAS mengambil kira masa bagi menentukan kelajuan purata kenderaan tersebut. Masa merupakan minit dibahagi dengan 60 kerana ada 60 minit dalam sejam dan kita hendak alihkan kelajuan kepada km/j.

Contoh A:

Katakanlah kenderaan itu meliputi perjalanan 100 km dalam 30 minit. Had laju laluan ini ialah 110 km/j.

Kelajuan purata = 100 km / (30 minit / 60) jam,

Oleh itu, kelajuan purata = 100 km / 0.5 jam = 200 km/j

Kena samanlah!

Contoh B:

Kenderaan itu meliputi laluan 100 km yang sama dalam 60 minit.

Kelajuan purata = 100 km / 60 minit,

Kelajuan purata = 100 km / (60 minit / 60),

Oleh itu, kelajuan purata = 100 km / 1 jam = 100 km/j

Ok, tidak disaman sebab kelajuan purata tidak melebihi had laju nasional.

Masa yang diperlukan dengan menepati had laju:

Ingat, sistem AWAS menjejak tempoh yang diambil oleh sesuatu kenderaan bergerak dari satu tempat ke satu tempat. Jadi apakah tempoh yang dikira ‘selamat’?

100 km = (X / 60) jam x 110 km/j,

(X / 60) = 100 km / 110 km/j = 0.909 jam,

Oleh yang demikian, X (masa purata) = 0.909 jam x 60 minit = 54.54 minit

Bagi menentukan samada pengiraan ini betul, kita lihat samada 54.54 minit itu dikali dengan 110 km/j bersamaan 100 km:

Jarak = 0.909 jam x 110 km/j = 100 km

TA-DAAA!!!

Jadi kita hendaklah melepasi kamera AWAS yang kedua itu 54.54 minit dari masa kita melepasi kamera AWAS yang pertama. Dengan formula ini, kita boleh rancang perjalanan kita, kerana kita dapat mengira masa yang diperlukan untuk meliputi sesuatu laluan dan sesuaikan kelajuan kenderaan kita.

Tetapi…

Sistem ini bukanlah penyelesaian yang sempurna, khususnya bagi mereka yang mahir dalam matematik dan tahu cara memanipulasi tempoh yang dikira dengan memecut laju pada bahagian tertentu, kemudian memperlahankan kenderaan untuk menepati masa yang ditetapkan—seolah-olah mereka mematuhi had laju. Malah, ini adalah teknik yang digunakan oleh pemandu rali ketika beralih antara Special Stages (SS) di jalan raya awam.

Atau yang lebih membimbangkan, ramai rakyat Malaysia tidak gemar kepada matematik dan/atau terlalu culas untuk mengira, lalu tidak tahu tempoh perjalanan yang diperlukan. Akibatnya, mereka mungkin memandu terlalu laju atau terlalu perlahan. Kita hanya kembali ke masalah asal.

Selain itu, anda tidak boleh tiba lebih awal daripada masa yang ditetapkan walaupun jalan kosong sepenuhnya. Ini bakal memanjangkan tempoh perjalanan dan mungkin menyebabkan kepada kesesakan lalu lintas.

Jadi, apakah pendapat anda? Sistem yang sedia ada lebih baik atau sistem baharu ini kelak?

Druid Motorcycles AI to Maximize Electric Motorcycle Range

Druid Motorcycles will arm their future electric motorcycles with AI (artificial intelligence).
The swarm AI system monitors many criteria and provide the best riding solution.
Motorcycles will get smarter and smarter.

The next wave of motorcycles, especially electric motorcycles, will be managed by AI (artificial intelligence) to maximize range and continuously improve performance. That is what Druid Motorcycles aim to do.

That way, you could travel further and faster without guessing what will happen next.

FROM DRUID MOTORCYCLES PRESS RELEASE

Swarm algorithm connects all DRUID MOTORCYCLES over the air to maximize range and continuously improve performance.

In addition to being maker of the world’s first 3D printed hybrid electric sports bikes Druid introduces continuous cloud connectivity on its revolutionary Prophet^TMplatform.

Druid’s System Warning and Referral Matrix (SWARM) A.I. system continuously reads ride data such as speed, energy consumption and altitude, and shares it in real time to up to 1000 individual Druid motorcycles., Powerful machine learning algorithms then adjust the the bike for weather, hills and riding style, and to maximize performance and extend the range of the electric batteries.

“I was a part time beekeeper when I was young,” says founder and lead designer Milan Svoboda. “From the bees I learned that individuals communicating even basic telemetry information can improve efficiency by an order of magnitude. The software technology today makes this systemic data sharing possible for motorcycles.”

The Druid Sorcerer HybridTM and Sorcerer XEVTM will be available for pre-order this May starting at a launch event in Austin, Texas. VISIT DRUID MOTORCYCLES for updates.