Autonomous Vehicles - Navigating the Road Ahead

Many companies are betting on autonomous cars to improve road safety. Autonomous vehicles don’t tire or become intoxicated while at the same time reducing distracted driving – a leading cause of accidents today.

However, many challenges still stand in the way of widescale adoption of autonomous vehicles (AVs). This short film explores Mcity – the world’s first purpose-built facility dedicated solely to testing them at University of Michigan – and its facilities.

Table of Contents

Sensors

Autonomous Vehicles use various sensors to collect information on their driving environment, such as cameras, radar and light detection and ranging (LIDAR). These can detect things like lane markings, curbs and pedestrians as well as vehicles in close proximity.

With sensors AVs can determine how best to move, whether that be maintaining a safe distance between vehicles or maintaining an even speed to avoid backups on roadways. These systems also help reduce congestion and fuel consumption by keeping cars safely within their own lanes.

Automated vehicles (AVs) offer the potential to help reduce risky driver behaviors like distracted or fatigued driving, with government data attributing driver behavior as the cause of 94% of collisions. Through automated braking technology, these accidents could become less frequent. You can listen to more about this on Changing Lanes, BMW’s weekly podcast. We can be found across major platforms – so subscribe and stay up-to-date!

Maps

Autonomous vehicles rely on sensors and high-definition maps to understand their surroundings, creating an internal representation of both their immediate surroundings as well as those nearby roadways. This data helps the autonomous car respond to road conditions quickly while adhering to traffic rules such as following lanes dividers or passing other cars safely.

HD maps are essential to autonomous driving as they allow vehicles to remain centimeter-accurate on the road, helping prevent human drivers from making costly errors such as misjudging a curb or misplacing a lane marker in bad weather conditions.

TomTom is committed to developing high-definition maps for autonomous vehicles, with their ADAS Map released in 2012. It contains numerous attributes for supporting vehicle sensor information including lane models, road furniture and geometry – delivering them directly through AutoStream so the autonomous vehicle can have up-to-the-second navigation guidance and warnings of speed limits and traffic signs.

Communication

Autonomous Vehicles depend on wireless communication technology for instant data transfer, driving behavior optimization suggestions, and emergency responses. These communications may take three forms: vehicle-to-vehicle, infrastructure-to-vehicle or device-to-vehicle.

Self-driving cars rely on three main electronic “eyes” – radar sensors, video cameras and laser-based Lidar – to build an internal map of their surroundings. This data is fed into onboard processors which combine hard-coded rules with obstacle avoidance algorithms and predictive modeling in order to provide instructions to actuators that control acceleration, braking and steering.

Consumer appetite for leaving the driving to technology is on the rise, prompting automakers to expand their ADAS offerings and move into fully autonomous vehicles. Both industry and government will face new challenges as transportation becomes more automated; driverless taxis will significantly alter employment patterns and state tax revenue streams while leaving unanswered questions regarding social and economic impacts of autonomous vehicles – so in this ZDNet / TechRepublic special feature we explore both their pros and cons that promise smoother commutes while increasing safety and efficiency.

Safety

Autonomous vehicles use three main electronic “eyes”, including radar, cameras and laser-based Lidar (Light Detection and Ranging). All this data is then fed into on-board processors that then use sophisticated software, algorithms and machine learning techniques to provide instructions for acceleration, braking and steering.

However, driver error still accounts for 94% of accidents; autonomous vehicles (AVs) are no exception to this trend; but given their proven ability to cover hundreds of millions of miles without fatal accidents without human interference suggests their performance has vastly outshone human driving capabilities.

Many automobile manufacturers are working toward Level 3 and higher automation, while technology firms like Waymo, GM’s Super Cruise, Ford BlueCruise, and Tesla Full Self Driving already have commercial fleets out on the road. Startup robo-taxi companies like May Mobility and TuSimple provide ride services while companies such as Gatik Robotic and Kodiak Robotic are adding autonomous tech into semi trucks for long hauling or local deliveries – ultimately becoming integral parts of intelligent transportation solutions.